fix to work bitsandbytes optimizers with full path #1640

Author: kohya-ss

library/train_util.py

@@ -3014,7 +3014,11 @@ def int_or_float(value):
         "--optimizer_type",
         type=str,
         default="",
-        help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, Lion, AdEMAMix8bit, PagedAdEMAMix8bit, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor",
+        help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, "
+        "Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, "
+        "DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, "
+        "AdaFactor. "
+        "Also, you can use any optimizer by specifying the full path to the class, like 'bitsandbytes.optim.AdEMAMix8bit' or 'bitsandbytes.optim.PagedAdEMAMix8bit'.",
     )
 
     # backward compatibility
@@ -4105,6 +4109,7 @@ def get_optimizer(args, trainable_params):
 
     lr = args.learning_rate
     optimizer = None
+    optimizer_class = None
 
     if optimizer_type == "Lion".lower():
         try:
@@ -4162,7 +4167,8 @@ def get_optimizer(args, trainable_params):
                     "No PagedLion8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedLion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください"
                 )
 
-        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+        if optimizer_class is not None:
+            optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
     elif optimizer_type == "PagedAdamW".lower():
         logger.info(f"use PagedAdamW optimizer | {optimizer_kwargs}")
@@ -4338,6 +4344,7 @@ def get_optimizer(args, trainable_params):
         optimizer_class = getattr(optimizer_module, optimizer_type)
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
+    # for logging
     optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__
     optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()])