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on
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Running
on
Zero
| import os | |
| import warnings | |
| import shutil | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig | |
| import torch | |
| from oryx.model import * | |
| from oryx.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", overwrite_config=None): | |
| 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 | |
| if "oryx" in model_name.lower(): | |
| # Load Oryx model | |
| if "7b" in model_name.lower(): | |
| from oryx.model.language_model.oryx_qwen import OryxQwenConfig | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| if overwrite_config is not None: | |
| cfg_pretrained = OryxQwenConfig.from_pretrained(model_path) | |
| print(f"Overwriting config with {overwrite_config}") | |
| for k, v in overwrite_config.items(): | |
| setattr(cfg_pretrained, k, v) | |
| model = OryxQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) | |
| else: | |
| model = OryxQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) | |
| else: | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
| cfg_pretrained = AutoConfig.from_pretrained(model_path) | |
| if overwrite_config is not None: | |
| print(f"Overwriting config with {overwrite_config}") | |
| for k, v in overwrite_config.items(): | |
| setattr(cfg_pretrained, k, v) | |
| model = OryxLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, **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.bfloat16, 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.bfloat16) | |
| 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) | |
| image_processor = None | |
| assert "oryx" in model_name.lower(), "Only Oryx models are supported for video chatbot." | |
| 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() | |
| print("Loading vision tower....") | |
| if not vision_tower.is_loaded: | |
| vision_tower.load_model(device_map=device_map) | |
| # if device_map != "auto": | |
| # vision_tower = vision_tower.bfloat16() | |
| # vision_tower = vision_tower.to("cuda") | |
| # else: | |
| # vision_tower.to(device="cuda:0", dtype=torch.bfloat16) | |
| image_processor = vision_tower.image_processor | |
| print("Loading vision tower succeeded.") | |
| 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 | |