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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 | |
| from transformers.models.clip.image_processing_clip import CLIPImageProcessor | |
| import torch | |
| from mplug_docowl.model import * | |
| from icecream import ic | |
| 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 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 'paperowl' or 'docowl' in model_name.lower(): | |
| if model_base is not None: | |
| # this may be mm projector only | |
| print('Loading mPLUG-DocOwl from base model...') | |
| tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) | |
| cfg_pretrained = AutoConfig.from_pretrained(model_path) | |
| model = MPLUGDocOwlLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| model = MPLUGDocOwlLlamaForCausalLM.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) | |
| # vision_tower = model.get_model().vision_model | |
| # vision_tower.to(device=device, dtype=torch.float16) | |
| image_processor = CLIPImageProcessor.from_pretrained(model_path) | |
| 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 |