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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.bfloat16, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| ) | |
| else: | |
| kwargs["torch_dtype"] = torch.bfloat16 | |
| # Check if model is LLaVA-based (including VisCoT which is built on LLaVA) | |
| if "llava" in model_name.lower() or "viscot" in model_name.lower(): | |
| # Load LLaVA model | |
| if "lora" in model_name.lower() and model_base is not None: | |
| raise NotImplementedError | |
| elif model_base is not None: | |
| raise NotImplementedError | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| model = LlavaLlamaForCausalLM.from_pretrained( | |
| model_path, low_cpu_mem_usage=True, **kwargs | |
| ) | |
| else: | |
| raise NotImplementedError | |
| image_processor = None | |
| if "llava" in model_name.lower() or "viscot" 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.bfloat16) | |
| if hasattr(vision_tower, "image_processor"): | |
| image_processor = vision_tower.image_processor | |
| else: | |
| image_processor = [ | |
| vision_tower.image_processor_0, | |
| vision_tower.image_processor_1, | |
| ] | |
| 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 | |