| | 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 |
| |
|
