text-generation-inference/backends/gaudi/server/text_generation_server/utils/peft.py

69 lines
2.1 KiB
Python
Raw Normal View History

import os
from typing import Union
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a Peft model. It might take a while without feedback")
try:
model = AutoPeftModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
except Exception:
model = AutoPeftModelForSeq2SeqLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
logger.info("Peft model detected.")
logger.info("Merging the lora weights.")
base_model_id = model.peft_config["default"].base_model_name_or_path
model = model.merge_and_unload()
os.makedirs(model_id, exist_ok=True)
cache_dir = model_id
logger.info(f"Saving the newly created merged model to {cache_dir}")
tokenizer = AutoTokenizer.from_pretrained(
base_model_id, trust_remote_code=trust_remote_code
)
model.save_pretrained(cache_dir, safe_serialization=True)
model.config.save_pretrained(cache_dir)
tokenizer.save_pretrained(cache_dir)
def download_peft(
model_id: Union[str, os.PathLike], revision: str, trust_remote_code: bool
):
torch_dtype = torch.float16
try:
_model = AutoPeftModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
except Exception:
_model = AutoPeftModelForSeq2SeqLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=True,
)
logger.info("Peft model downloaded.")