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| import os | |
| import warnings | |
| import shutil | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig, CLIPImageProcessor | |
| import torch | |
| from llava_phi.model import * | |
| from llava_phi.constants import 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 | |