|
|
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 |
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
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/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, |
|
|
|
|
|
**kwargs, |
|
|
) |
|
|
model = model.to(device=device) |
|
|
|
|
|
image_processor = None |
|
|
model.resize_token_embeddings(len(tokenizer)) |
|
|
|
|
|
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 |
|
|
|
