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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from llava.model import LlavaMistralForCausalLM
from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
kwargs = {}
if device != "cuda":
kwargs['device_map'] = {"": device}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
)
else:
kwargs['torch_dtype'] = torch.float16
if 'llava' in model_name.lower():
# Load LLaVA model
if 'mistral' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=False,
use_flash_attention_2=False,
**kwargs
)
else:
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print('Convert to FP16...')
model.to(torch.float16)
else:
use_fast = False
if 'mpt' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
image_processor = None
if 'llava' in model_name.lower(): # or 'mistral' in model_name.lower():
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(device=device, dtype=torch.float16)
model.model.mm_projector.to(device=device, dtype=torch.float16)
model.to(device=device, dtype=torch.float16)
image_processor = vision_tower.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
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