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patch fix for lora training #3604

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1 change: 0 additions & 1 deletion model/model_training/models/peft_modeling.py
Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,6 @@ def peft_model(model, training_config):
"lora_dropout": 0.05,
"bias": "none",
"task_type": "CAUSAL_LM",
"modules_to_save": ["wte", "lm_head"],
}
kwargs = merge_dicts(default_args, peft_config)
if kwargs.get("target_modules") == "all":
Expand Down
15 changes: 12 additions & 3 deletions model/model_training/trainer_sft.py
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
from torch import nn
from torch.utils.data import DataLoader, Subset
from tqdm import tqdm
from transformers import PreTrainedModel, Trainer, TrainingArguments
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, Trainer, TrainingArguments
from transformers.trainer_pt_utils import IterableDatasetShard
from transformers.trainer_utils import seed_worker
from transformers.training_args import OptimizerNames
Expand Down Expand Up @@ -327,7 +327,10 @@ def main():

init_rng(training_conf)

tokenizer = get_tokenizer(training_conf)
if training_conf.peft_model:
tokenizer = AutoTokenizer.from_pretrained(training_conf.model_name)
else:
tokenizer = get_tokenizer(training_conf)

if not training_conf.deepspeed or training_conf.local_rank == 0:
tokenizer_sanity_check(tokenizer)
Expand Down Expand Up @@ -416,7 +419,13 @@ def main():
sampler = None

metrics, preprocess_fns = get_metrics(training_conf, tokenizer)
model = get_model(training_conf, tokenizer)
if training_conf.peft_model:
logging.warning("PEFT model: make sure this is an adapted base model which has added special tokens!")
model = AutoModelForCausalLM.from_pretrained(
training_conf.model_name, torch_dtype=torch.bfloat16 if training_conf.dtype == "bf16" else torch.float16
)
else:
model = get_model(training_conf, tokenizer)

superhot = RopePatch.from_config(training_conf) if training_conf.superhot else None
if superhot:
Expand Down