import os import warnings from transformers import ( AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig, CLIPImageProcessor, ) import torch from .language_model.llava_phi import LlavaPhiForCausalLM from .language_model.configuration_llava_phi import LlavaPhiConfig IGNORE_INDEX = -100 IMAGE_TOKEN_INDEX = -200 DEFAULT_IMAGE_TOKEN = "" DEFAULT_IMAGE_PATCH_TOKEN = "" DEFAULT_IM_START_TOKEN = "" DEFAULT_IM_END_TOKEN = "" def load_pretrained_model( model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="cuda", device="cuda", ): kwargs = {"device_map": device_map} 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: # TODO: after fine-tuning LLava-Phi, load the model weights with fp16 will pose nan # kwargs['torch_dtype'] = torch.float16 if "phi" in model_name.lower(): # Load LLaVA-Phi model if "lora" in model_name.lower() and model_base is None: warnings.warn( "There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument." ) if "lora" in model_name.lower() and model_base is not None: lora_cfg_pretrained = AutoConfig.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) print("Loading LLaVA-Phi from base model...") model = LlavaPhiForCausalLM.from_pretrained( model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **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 ) ) print("Loading additional LLaVA-Phi 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", ) else: # this is probably from HF Hub from huggingface_hub import hf_hub_download def load_from_hf(repo_id, filename, subfolder=None): cache_file = hf_hub_download( repo_id=repo_id, filename=filename, subfolder=subfolder ) return torch.load(cache_file, map_location="cpu") non_lora_trainables = load_from_hf( model_path, "non_lora_trainables.bin" ) 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: # this may be mm projector only print("Loading LLaVA-Phi from base model...") tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) cfg_pretrained = AutoConfig.from_pretrained(model_path) model = LlavaPhiForCausalLM.from_pretrained( model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs ) mm_projector_weights = torch.load( os.path.join(model_path, "mm_projector.bin"), map_location="cpu" ) mm_projector_weights = { k: v.to(torch.float16) for k, v in mm_projector_weights.items() } model.load_state_dict(mm_projector_weights, strict=False) else: print("load llaVA-Phi MLLM!!!") config = LlavaPhiConfig.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) model = LlavaPhiForCausalLM.from_pretrained( model_path, config=config, use_safetensors=True, **kwargs ).to("cuda") else: # Load language model if model_base is not None: # PEFT model from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) model = AutoModelForCausalLM.from_pretrained( model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto", ) print(f"Loading LoRA weights from {model_path}") model = PeftModel.from_pretrained(model, model_path) print(f"Merging weights") model = model.merge_and_unload() print("Convert to FP16...") model.to(torch.float16) else: tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained( model_path, low_cpu_mem_usage=True, **kwargs ) image_processor = CLIPImageProcessor.from_pretrained(model_path) if "phi" in model_name.lower(): mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) # TODO: the tokenizer length of phi-2 is 50295, but the output class of lm_head is 51200 if mm_use_im_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) if mm_use_im_start_end: tokenizer.add_tokens( [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True ) # model.resize_token_embeddings(len(tokenizer)) else: raise ValueError(f"Unsupported model name: {model_name}") if hasattr(model.config, "max_sequence_length"): context_len = model.config.max_sequence_length else: context_len = 2048 model.to(device="cuda") print(kwargs) return tokenizer, model, image_processor, context_len