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import os
import warnings
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig, AutoProcessor
import torch
from ola.model import *
from ola.model.speech_encoder.builder import build_speech_encoder
# 过滤掉 PyTorch 的 meta parameter 警告
warnings.filterwarnings("ignore", message=".*copying from a non-meta parameter in the checkpoint to a meta parameter.*")
def load_pretrained_model(model_path, model_type, model_base, is_lora=False, s2s=False, load_8bit=False, load_4bit=False, device="cuda", use_flash_attn=False, **kwargs):
device = "cuda"
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.bfloat16
if use_flash_attn:
kwargs['attn_implementation'] = 'flash_attention_2'
if model_type == 'ola_internvl':
model_cls = OlaQwen3ForCausalLM
print('Loading OlaQwen3ForCausalLM model...')
else:
model_cls = OlaQwenForCausalLM
# Load Ola model
if is_lora:
assert model_base is not None, "model_base is required for LoRA models."
from ola.model.language_model.ola_qwen import OlaConfigQwen
lora_cfg_pretrained = OlaConfigQwen.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print('Loading Ola from base model...')
model = model_cls.from_pretrained(model_base, low_cpu_mem_usage=False, config=lora_cfg_pretrained, **kwargs)
print('Loading additional Ola 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')
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, assign=True)
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:
print('Loading Ola from base model...')
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = model_cls.from_pretrained(model_base, low_cpu_mem_usage=False, config=cfg_pretrained, **kwargs)
speech_projector_weights = torch.load(os.path.join(model_path, 'speech_projector.bin'), map_location='cpu')
speech_projector_weights = {k: v.to(torch.float16) for k, v in speech_projector_weights.items()}
model.load_state_dict(speech_projector_weights, strict=False, assign=True)
model = model.to(device=device)
else:
# model_path = "/data1/cxy/plm-v/modeling/plm_internvl3_5_ola"
model_path = "/data1/cxy/plm-v/modeling/ckpt/ola_audio_8_8gpu/checkpoint-120"
tokernizer_path = "/data1/cxy/plm-v/modeling/internvl3_5-2B"
tokenizer = AutoTokenizer.from_pretrained(tokernizer_path, use_fast=False, trust_remote_code=True)
cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
with torch.device("cuda"):
model = model_cls.from_pretrained(
model_path,
trust_remote_code=True,
config=cfg,
# device_map="auto",
**kwargs,
)
model = model.to(device=device)
# breakpoint()
image_processor = None
model.resize_token_embeddings(len(tokenizer))
# breakpoint()
print("Loading vision tower...")
print("Loading vision tower succeeded.")
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 16384
image_processor = AutoProcessor.from_pretrained("/data1/cxy/plm-v/modeling/internvl3_5-2B-HF")
return tokenizer, model, image_processor, context_len
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