# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright: # Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import warnings import shutil import torch from transformers import PretrainedConfig, AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig from . import * from .multimodal_projector import load_mm_projector from ..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="auto", device="cuda", use_flash_attn=False, **kwargs): if 'token' in kwargs: token = kwargs['token'] else: token = None kwargs = {"device_map": device_map, **kwargs} if device != "cuda": kwargs['device_map'] = {"": device} 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.float16 if use_flash_attn: kwargs['attn_implementation'] = 'flash_attention_2' if "videollama" in model_name.lower(): # Load LLaVA 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. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.') 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 VideoLLaMA from base model...') model = Videollama2LlamaForCausalLM.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 VideoLLaMA 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 or '-base' in model_name.lower(): # loading vision-language projector print('Loading VideoLLaMA 2 from base model...') cfg_pretrained = PretrainedConfig.from_pretrained(model_path, token=token) # NOTE: AutoConfig will modify `_name_or_path` property to `model_path` if `model_path` is not None. # cfg_pretrained = AutoConfig.from_pretrained(model_path, token=token) model_base = model_base if model_base is not None else cfg_pretrained._name_or_path tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False, token=token) if 'vicuna' in model_name.lower(): model = Videollama2LlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) elif 'mixtral' in model_name.lower(): model = Videollama2MixtralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) else: model = Videollama2MistralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) # NOTE: old codes for loading local mm_projector.bin # 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) # NOTE: new codes which supports loading mm_projector.bin both offline and online mm_projector_weights = load_mm_projector(model_path, token=token) model.load_state_dict(mm_projector_weights, strict=False) else: if 'vicuna' in model_name.lower(): tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, token=token) model = Videollama2LlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) elif 'mixtral' in model_name.lower(): tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, token=token) model = Videollama2MixtralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) else: # NOTE: mistral-based model is our default model. tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, token=token) model = Videollama2MistralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **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) model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs) 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: use_fast = False tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) processor = None if "videollama" 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.float16) # NOTE: videollama2 adopts the same processor for processing image and video. 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, processor, context_len