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Running
on
Zero
| import os | |
| import warnings | |
| import shutil | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig | |
| import torch | |
| from ola.model import * | |
| from ola.model.speech_encoder.builder import build_speech_encoder | |
| def load_pretrained_model(model_path, model_base, is_lora=False, s2s=False, load_8bit=False, load_4bit=False, device="cuda", use_flash_attn=False, **kwargs): | |
| 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' | |
| model_cls = OlaQwenForCausalLM | |
| # Load OmniSpeech 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 OmniSpeech from base model...') | |
| model = model_cls.from_pretrained(model_base, low_cpu_mem_usage=False, config=lora_cfg_pretrained, **kwargs) | |
| print('Loading additional OmniSpeech 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) | |
| 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 OmniSpeech 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) | |
| model = model.to(device=device) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| model = model_cls.from_pretrained( | |
| model_path, | |
| low_cpu_mem_usage=False, | |
| **kwargs | |
| ) | |
| model = model.to(device=device) | |
| model.get_model().speech_encoder = build_speech_encoder(model.config) | |
| model.get_model().speech_encoder.to(device=device, dtype=torch.float16) | |
| image_processor = None | |
| model.resize_token_embeddings(len(tokenizer)) | |
| vision_tower = model.get_vision_tower() | |
| print("Loading vision tower...") | |
| if not vision_tower.is_loaded: | |
| vision_tower.load_model(device_map=device) | |
| if device != "auto": | |
| vision_tower.to(device="cuda", dtype=torch.bfloat16) | |
| else: | |
| vision_tower.to(device="cuda:0", dtype=torch.bfloat16) | |
| image_processor = vision_tower.image_processor | |
| print("Loading vision tower succeeded.") | |
| if hasattr(model.config, "max_sequence_length"): | |
| context_len = model.config.max_sequence_length | |
| else: | |
| context_len = 16384 | |
| return tokenizer, model, image_processor, context_len | |