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import os
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from vtimellm.model import *
from peft import PeftModel
def load_lora(model, lora_path):
non_lora_trainables_path = os.path.join(lora_path, 'non_lora_trainables.bin')
if os.path.exists(non_lora_trainables_path):
non_lora_trainables = torch.load(non_lora_trainables_path, 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, lora_path)
return model
def load_pretrained_model(args, stage2=None, stage3=None, stage4=None, stage5=None):
kwargs = {'torch_dtype': torch.float16}
# model_path = os.path.expanduser(args.model_path)
model_base = args.model_base
# lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
print('Loading VTimeLLM from base model...')
if 'chatglm' in model_base:
tokenizer = AutoTokenizer.from_pretrained(model_base, trust_remote_code=True)
model = VTimeLLMChatGLMForCausalLM.from_pretrained(model_base)
else:
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = VTimeLLMLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **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))
# load stage1:
model = model.cuda()
model.get_model().initialize_vision_modules(args)
if stage2 is not None and stage2 != "":
print('Loading stage2 weights...')
model = load_lora(model, stage2)
print('Merging stage2 weights...')
model = model.merge_and_unload()
if stage3 is not None and stage3 != "" :
print('Loading stage3 weights...')
model = load_lora(model, stage3)
print('Merging stage3 weights...')
model = model.merge_and_unload()
if stage4 is not None and stage4 != "":
print('Loading stage4 weights...')
model = load_lora(model, stage4)
print('Merging stage4 weights...')
model = model.merge_and_unload()
if stage5 is not None and stage5 != "":
print('Loading stage5 weights...')
model = load_lora(model, stage5)
print('Merging stage5 weights...')
model = model.merge_and_unload()
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, context_len
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