Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

patch fix for lora training #3604

Draft
wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

patch fix for lora training #3604

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1d48cf1
patch fix for lora training
Jul 25, 2023
d55ff36
no message
Jul 25, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion model/model_training/models/peft_modeling.py
View file
Open in desktop
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
View file
Open in desktop
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