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| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig | |
| import torch | |
| from medrax.llava.model import LlavaMistralForCausalLM | |
| from medrax.llava.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_in_8bit=False, | |
| load_in_4bit=True, | |
| device="cuda", | |
| cache_dir: str = "/model-weights", | |
| low_cpu_mem_usage=True, | |
| torch_dtype=torch.bfloat16, | |
| ): | |
| kwargs = {} | |
| if device != "cuda": | |
| kwargs["device_map"] = {"": device} | |
| # else: | |
| # kwargs["device_map"] = "auto" | |
| if load_in_8bit: | |
| kwargs["load_in_8bit"] = True | |
| elif load_in_4bit: | |
| # kwargs["load_in_4bit"] = True | |
| kwargs["quantization_config"] = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_compute_dtype=torch_dtype, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| ) | |
| # else: | |
| # kwargs["torch_dtype"] = torch_dtype | |
| if "llava" in model_name.lower(): | |
| # Load LLaVA model | |
| if "mistral" in model_name.lower(): | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, cache_dir=cache_dir) | |
| model = LlavaMistralForCausalLM.from_pretrained( | |
| model_path, | |
| low_cpu_mem_usage=low_cpu_mem_usage, | |
| use_flash_attention_2=False, | |
| cache_dir=cache_dir, | |
| torch_dtype=torch_dtype, | |
| **kwargs, | |
| ) | |
| 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, cache_dir=cache_dir | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_base, | |
| low_cpu_mem_usage=True, | |
| cache_dir=cache_dir, | |
| torch_dtype=torch_dtype, | |
| **kwargs, | |
| ) | |
| print(f"Loading LoRA weights from {model_path}") | |
| model = PeftModel.from_pretrained(model, model_path) | |
| print("Merging weights") | |
| model = model.merge_and_unload() | |
| print("Convert to FP16...") | |
| model.to(torch_dtype) | |
| else: | |
| use_fast = False | |
| if "mpt" in model_name.lower(): | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_path, use_fast=True, cache_dir=cache_dir | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| low_cpu_mem_usage=True, | |
| trust_remote_code=True, | |
| cache_dir=cache_dir, | |
| torch_dtype=torch_dtype, | |
| **kwargs, | |
| ) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_path, use_fast=False, cache_dir=cache_dir | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| low_cpu_mem_usage=True, | |
| cache_dir=cache_dir, | |
| torch_dtype=torch_dtype, | |
| **kwargs, | |
| ) | |
| image_processor = None | |
| if "llava" in model_name.lower(): # or 'mistral' 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) | |
| 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)) | |
| vision_tower = model.get_vision_tower() | |
| if not vision_tower.is_loaded: | |
| vision_tower.load_model() | |
| vision_tower.to(device=device, dtype=torch_dtype) | |
| model.model.mm_projector.to(device=device, dtype=torch_dtype) | |
| if not (load_in_4bit or load_in_8bit): | |
| model.to(device=device, dtype=torch_dtype) | |
| image_processor = vision_tower.image_processor | |
| if hasattr(model.config, "max_sequence_length"): | |
| context_len = model.config.max_sequence_length | |
| else: | |
| context_len = 2048 | |
| return tokenizer, model, image_processor, context_len | |