add grad_hook after restore state closes #1344

sdxl_train.py

@@ -481,6 +481,26 @@ def train(args):
             text_encoder2 = accelerator.prepare(text_encoder2)
         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:
+        # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
+        text_encoder1.to("cpu", dtype=torch.float32)
+        text_encoder2.to("cpu", dtype=torch.float32)
+        clean_memory_on_device(accelerator.device)
+    else:
+        # make sure Text Encoders are on GPU
+        text_encoder1.to(accelerator.device)
+        text_encoder2.to(accelerator.device)
+
+    # 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:
+        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
+        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
+
     if args.fused_backward_pass:
         # use fused optimizer for backward pass: other optimizers will be supported in the future
         import library.adafactor_fused
@@ -532,26 +552,6 @@ def train(args):
                         parameter_optimizer_map[parameter] = opt_idx
                         num_parameters_per_group[opt_idx] += 1
 
-    # 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:
-        # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
-        text_encoder1.to("cpu", dtype=torch.float32)
-        text_encoder2.to("cpu", dtype=torch.float32)
-        clean_memory_on_device(accelerator.device)
-    else:
-        # make sure Text Encoders are on GPU
-        text_encoder1.to(accelerator.device)
-        text_encoder2.to(accelerator.device)
-
-    # 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:
-        # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do.
-        # -> But we think it's ok to patch accelerator even if deepspeed is enabled.
-        train_util.patch_accelerator_for_fp16_training(accelerator)
-
-    # resumeする
-    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
-
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
@@ -589,7 +589,11 @@ def train(args):
             init_kwargs["wandb"] = {"name": args.wandb_run_name}
         if args.log_tracker_config is not None:
             init_kwargs = toml.load(args.log_tracker_config)
-        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs)
+        accelerator.init_trackers(
+            "finetuning" if args.log_tracker_name is None else args.log_tracker_name,
+            config=train_util.get_sanitized_config_or_none(args),
+            init_kwargs=init_kwargs,
+        )
 
     # For --sample_at_first
     sdxl_train_util.sample_images(