Merge branch 'dev' into min-SNR

fine_tune.py

@@ -6,6 +6,7 @@ import gc
 import math
 import os
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -22,10 +23,6 @@ from library.config_util import (
 import library.custom_train_functions as custom_train_functions
 from library.custom_train_functions import apply_snr_weight 
 
-def collate_fn(examples):
-    return examples[0]
-
-
 def train(args):
     train_util.verify_training_args(args)
     train_util.prepare_dataset_args(args, True)
@@ -65,6 +62,10 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value('i',0)
+    current_step = Value('i',0)
+    collater = train_util.collater_class(current_epoch,current_step)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -188,7 +189,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collate_fn,
+        collate_fn=collater,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )
@@ -198,6 +199,9 @@ def train(args):
         args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
     # lr schedulerを用意する
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
@@ -256,13 +260,14 @@ def train(args):
 
     for epoch in range(num_train_epochs):
         print(f"epoch {epoch+1}/{num_train_epochs}")
-        train_dataset_group.set_current_epoch(epoch + 1)
+        current_epoch.value = epoch+1
 
         for m in training_models:
             m.train()
 
         loss_total = 0
         for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
             with accelerator.accumulate(training_models[0]):  # 複数モデルに対応していない模様だがとりあえずこうしておく
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None: