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raise error if options conflict

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Kohya S
2023-02-23 21:35:47 +09:00
parent 0eb01dea55
commit 9b13444b9c
1 changed files with 10 additions and 5 deletions
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15
library/train_util.py
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@@ -1372,8 +1372,8 @@ def add_sd_models_arguments(parser: argparse.ArgumentParser):
def add_optimizer_arguments(parser: argparse.ArgumentParser):
parser.add_argument("--optimizer_type", type=str, default="AdamW",
help="Optimizer to use / オプティマイザの種類: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation, AdaFactor")
parser.add_argument("--optimizer_type", type=str, default="",
help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation, AdaFactor")
# backward compatibility
parser.add_argument("--use_8bit_adam", action="store_true",
@@ -1532,11 +1532,16 @@ def get_optimizer(args, trainable_params):
optimizer_type = args.optimizer_type
if args.use_8bit_adam:
print(f"*** use_8bit_adam option is specified. optimizer_type is ignored / use_8bit_adamオプションが指定されているためoptimizer_typeは無視されます")
assert not args.use_lion_optimizer, "both option use_8bit_adam and use_lion_optimizer are specified / use_8bit_adamとuse_lion_optimizerの両方のオプションが指定されています"
assert optimizer_type is None or optimizer_type == "", "both option use_8bit_adam and optimizer_type are specified / use_8bit_adamとoptimizer_typeの両方のオプションが指定されています"
optimizer_type = "AdamW8bit"
elif args.use_lion_optimizer:
print(f"*** use_lion_optimizer option is specified. optimizer_type is ignored / use_lion_optimizerオプションが指定されているためoptimizer_typeは無視されます")
assert optimizer_type is None or optimizer_type == "", "both option use_lion_optimizer and optimizer_type are specified / use_lion_optimizerとoptimizer_typeの両方のオプションが指定されています"
optimizer_type = "Lion"
if optimizer_type is None or optimizer_type == "":
optimizer_type = "AdamW"
optimizer_type = optimizer_type.lower()
# 引数を分解する：boolとfloat、tupleのみ対応
@@ -1557,7 +1562,7 @@ def get_optimizer(args, trainable_params):
value = tuple(value)
optimizer_kwargs[key] = value
print("optkwargs:", optimizer_kwargs)
# print("optkwargs:", optimizer_kwargs)
lr = args.learning_rate