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| # 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 | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig | |
| import torch | |
| from llava.model import * | |
| from 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_8bit=False, load_4bit=False, device_map="auto", 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: | |
| kwargs['torch_dtype'] = torch.float16 | |
| if 'llava' 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 LLaVA from base model...') | |
| model = LlavaLlamaForCausalLM.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 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 from base model...') | |
| if 'mpt' in model_name.lower(): | |
| if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')): | |
| shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py')) | |
| tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True) | |
| cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True) | |
| model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) | |
| cfg_pretrained = AutoConfig.from_pretrained(model_path) | |
| model = LlavaLlamaForCausalLM.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: | |
| if 'mpt' in model_name.lower(): | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) | |
| model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| model = LlavaLlamaForCausalLM.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, 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: | |
| use_fast = False | |
| if 'mpt' in model_name.lower(): | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) | |
| image_processor = None | |
| if 'llava' 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) | |
| 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 | |