Support new optimizer Schedule free (#1250)

* init

* use no schedule

* fix typo

* update for eval()

* fix typo

fine_tune.py

@@ -255,18 +255,31 @@ 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:
         # 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:
@@ -324,6 +337,8 @@ def train(args):
             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():
@@ -390,9 +405,13 @@ 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)

library/train_util.py

@@ -3087,7 +3087,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
     )
     parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed")
     parser.add_argument(
-        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / grandient checkpointingを有効にする"
+        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / gradient checkpointingを有効にする"
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
@@ -4087,6 +4087,21 @@ 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

@@ -415,9 +415,14 @@ def train(args):
             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:
@@ -428,7 +433,10 @@ def train(args):
             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)
 
     # TextEncoderの出力をキャッシュするときにはCPUへ移動する
     if args.cache_text_encoder_outputs:
@@ -503,6 +511,8 @@ def train(args):
             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):
                 if "latents" in batch and batch["latents"] is not None:
@@ -626,9 +636,13 @@ 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)

sdxl_train_control_net_lllite.py

@@ -286,11 +286,19 @@ 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
@@ -390,6 +398,8 @@ def train(args):
         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():
@@ -481,9 +491,13 @@ 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)

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 train(args):
         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 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)

train_controlnet.py

@@ -276,9 +276,14 @@ def train(args):
         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)
@@ -393,6 +398,8 @@ def train(args):
         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():
@@ -472,6 +479,9 @@ def train(args):
                 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)

train_network.py

@@ -420,9 +420,14 @@ class NetworkTrainer:
                 text_encoder2=text_encoders[1] if train_text_encoder and len(text_encoders) > 1 else None,
                 network=network,
             )
-            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_model = ds_model
         else:
             if train_unet:
@@ -437,15 +442,23 @@ class NetworkTrainer:
                     text_encoders = [text_encoder]
             else:
                 pass  # if text_encoder is not trained, no need to prepare. and device and dtype are already set
-
-            network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                network, optimizer, train_dataloader, lr_scheduler
-            )
+            
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                network, optimizer, train_dataloader = accelerator.prepare(
+                    network, optimizer, train_dataloader
+                )  
+            else:
+                network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                    network, optimizer, train_dataloader, lr_scheduler
+                )
             training_model = network
 
         if args.gradient_checkpointing:
             # according to TI example in Diffusers, train is required
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             unet.train()
+
             for t_enc in text_encoders:
                 t_enc.train()
 
@@ -454,6 +467,8 @@ class NetworkTrainer:
                     t_enc.text_model.embeddings.requires_grad_(True)
 
         else:
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
             unet.eval()
             for t_enc in text_encoders:
                 t_enc.eval()
@@ -804,6 +819,8 @@ class NetworkTrainer:
             accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
 
             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_model):
                     on_step_start(text_encoder, unet)
@@ -909,7 +926,8 @@ class NetworkTrainer:
                             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.scale_weight_norms:
@@ -920,6 +938,9 @@ class NetworkTrainer:
                 else:
                     keys_scaled, mean_norm, maximum_norm = None, None, None
 
+                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_textual_inversion.py

@@ -416,14 +416,24 @@ class TextualInversionTrainer:
 
         # acceleratorがなんかよろしくやってくれるらしい
         if len(text_encoders) == 1:
-            text_encoder_or_list, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                text_encoder_or_list, optimizer, train_dataloader, lr_scheduler
-            )
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                text_encoder_or_list, optimizer, train_dataloader = accelerator.preparet(
+                    text_encoder_or_list, optimizer, train_dataloader
+                )   
+            else:
+                text_encoder_or_list, optimizer, train_dataloader, lr_scheduler = accelerator.preparet(
+                    text_encoder_or_list, optimizer, train_dataloader, lr_scheduler
+                )
 
         elif len(text_encoders) == 2:
-            text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                text_encoders[0], text_encoders[1], optimizer, train_dataloader, lr_scheduler
-            )
+            if args.optimizer_type.lower().endswith("schedulefree"):
+                text_encoder1, text_encoder2, optimizer, train_dataloader = accelerator.prepare(
+                    text_encoders[0], text_encoders[1], optimizer, train_dataloader
+                )  
+            else:
+                text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+                    text_encoders[0], text_encoders[1], optimizer, train_dataloader, lr_scheduler
+                )
 
             text_encoder_or_list = text_encoders = [text_encoder1, text_encoder2]
 
@@ -452,8 +462,12 @@ class TextualInversionTrainer:
         unet.to(accelerator.device, dtype=weight_dtype)
         if args.gradient_checkpointing:  # according to TI example in Diffusers, train is required
             # TODO U-Netをオリジナルに置き換えたのでいらないはずなので、後で確認して消す
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             unet.train()
         else:
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.eval()
             unet.eval()
 
         if not cache_latents:  # キャッシュしない場合はVAEを使うのでVAEを準備する
@@ -557,6 +571,8 @@ class TextualInversionTrainer:
             loss_total = 0
 
             for step, batch in enumerate(train_dataloader):
+                if (args.optimizer_type.lower().endswith("schedulefree")):
+                    optimizer.train()
                 current_step.value = global_step
                 with accelerator.accumulate(text_encoders[0]):
                     with torch.no_grad():
@@ -627,6 +643,9 @@ class TextualInversionTrainer:
                                 index_no_updates
                             ]
 
+                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_textual_inversion_XTI.py

@@ -335,9 +335,14 @@ def train(args):
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, optimizer, train_dataloader, lr_scheduler
-    )
+    if args.optimizer_type.lower().endswith("schedulefree"):
+        text_encoder, optimizer, train_dataloader = accelerator.prepare(
+            text_encoder, optimizer, train_dataloader
+        )   
+    else:
+        text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            text_encoder, optimizer, train_dataloader, lr_scheduler
+        )
 
     index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
     # logger.info(len(index_no_updates), torch.sum(index_no_updates))
@@ -354,8 +359,12 @@ def train(args):
     unet.to(accelerator.device, dtype=weight_dtype)
     if args.gradient_checkpointing:  # according to TI example in Diffusers, train is required
         unet.train()
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.train()
     else:
         unet.eval()
+        if (args.optimizer_type.lower().endswith("schedulefree")):
+            optimizer.eval()
 
     if not cache_latents:
         vae.requires_grad_(False)
@@ -438,6 +447,8 @@ def train(args):
         loss_total = 0
 
         for step, batch in enumerate(train_dataloader):
+            if (args.optimizer_type.lower().endswith("schedulefree")):
+                optimizer.train()
             current_step.value = global_step
             with accelerator.accumulate(text_encoder):
                 with torch.no_grad():
@@ -496,7 +507,8 @@ 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)
 
                 # Let's make sure we don't update any embedding weights besides the newly added token
@@ -505,6 +517,9 @@ def train(args):
                         index_no_updates
                     ]
 
+            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)