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from peft import PeftModel
import torch
from transformers import BitsAndBytesConfig, Qwen2VLForConditionalGeneration, AutoProcessor, AutoConfig, Qwen2_5_VLForConditionalGeneration
import warnings
import os
import json
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
# This code is borrowed from LLaVA
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False,
device_map="auto", device="cuda", use_flash_attn=False, **kwargs):
kwargs = {"device_map": device_map}
if device != "cuda":
kwargs['device_map'] = {"":device}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
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 use_flash_attn:
kwargs['_attn_implementation'] = 'flash_attention_2'
if 'lora' in model_name.lower() and model_base is None:
warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument.')
if 'lora' in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
if hasattr(lora_cfg_pretrained, 'quantization_config'):
del lora_cfg_pretrained.quantization_config
processor = AutoProcessor.from_pretrained(model_base)
print('Loading Qwen2-VL from base model...')
if "Qwen2.5" in model_base:
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
else:
model = Qwen2VLForConditionalGeneration.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
print('Loading additional Qwen2-VL weights...')
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_state_dict.bin'), map_location='cpu')
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
if any(k.startswith('model.model.') for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
model.load_state_dict(non_lora_trainables, strict=False)
print('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Model Loaded!!!')
else:
with open(os.path.join(model_path, 'config.json'), 'r') as f:
config = json.load(f)
if "Qwen2.5" in config["_name_or_path"]:
processor = AutoProcessor.from_pretrained(model_path)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
processor = AutoProcessor.from_pretrained(model_path)
model = Qwen2VLForConditionalGeneration.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
return processor, model
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1] |