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update lora-sft

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This commit is contained in:
gongjy 2024-10-11 17:43:52 +08:00
parent 3a034a47c8
commit 36fadc7ef1
1 changed files with 7 additions and 9 deletions
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16
4-lora_sft.py
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@ -16,6 +16,7 @@ from peft import get_peft_model, LoraConfig, TaskType
from torch.utils.data import DataLoader
from model.LMConfig import LMConfig
from model.dataset import SFTDataset
from model.model import Transformer
warnings.filterwarnings('ignore')
@ -96,8 +97,6 @@ def find_all_linear_names(model):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names:
lora_module_names.remove('lm_head')
return list(lora_module_names)
@ -109,11 +108,7 @@ def init_model():
target_modules = find_all_linear_names(model)
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=8,
lora_alpha=16,
lora_dropout=0.1,
inference_mode=False,
target_modules=target_modules
)
model = get_peft_model(model, peft_config)
@ -126,7 +121,7 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MiniMind LoRA Fine-tuning")
parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
parser.add_argument("--epochs", type=int, default=20, help="Number of epochs")
parser.add_argument("--batch_size", type=int, default=16, help="Batch size")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use")
parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
@ -162,7 +157,7 @@ if __name__ == "__main__":
model, tokenizer = init_model()
df = pd.read_csv('./dataset/sft_data.csv')
df = pd.read_csv('./dataset/sft_data_single.csv')
df = df.sample(frac=1.0)
train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len)
train_loader = DataLoader(
@ -175,7 +170,10 @@ if __name__ == "__main__":
)
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
optimizer = optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=args.learning_rate
)
if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2:
Logger("compiling the model... (takes a ~minute)")