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Running
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Zero
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import os
import warnings
import torch
from transformers import AutoTokenizer, AutoConfig, BitsAndBytesConfig, logging, AutoModelForCausalLM
logging.set_verbosity_error()
def load_pretrained_model(model_path, model_base, model_name, model_type, load_8bit=False, load_4bit=False,
device_map="auto", device="cuda", **kwargs):
if model_type not in {'qwen1.5-1.8b', 'qwen1.5-0.5b'}:
raise ValueError(f"Unknown Model Type {model_type}")
kwargs = {"device_map": device_map, **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 '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)
print('Loading nanoLLaVA from base model...')
if model_type == 'qwen1.5-1.8b' or model_type == 'qwen1.5-0.5b':
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
model = AutoModelForCausalLM.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 nanoLLaVA weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
else:
# this is probably from HF Hub
from huggingface_hub import hf_hub_download
def load_from_hf(repo_id, filename, subfolder=None):
cache_file = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder)
return torch.load(cache_file, map_location='cpu')
non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
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)
from peft import PeftModel
print('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Model is loaded...')
elif model_base is not None:
# this may be mm projector only
print('Loading nanoLLaVA from base model...')
cfg_pretrained = AutoConfig.from_pretrained(model_path)
if model_type == 'qwen1.5-1.8b' or model_type == 'qwen1.5-0.5b':
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained,
**kwargs)
mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
if model_type == 'qwen1.5-1.8b' or model_type == 'qwen1.5-0.5b':
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
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)
image_processor = vision_tower.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
if model.generation_config.pad_token_id is None:
model.generation_config.pad_token_id = model.generation_config.eos_token_id
return tokenizer, model, image_processor, context_len |