DeQA-Score/src/model/builder.py

#    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 torch
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from transformers.models.clip.image_processing_clip import CLIPImageProcessor

from src.model import *


def load_pretrained_model(
    model_path,
    model_base,
    model_name,
    load_8bit=False,
    load_4bit=False,
    device_map="auto",
    device="cuda",
    preprocessor_path=None,
):
    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 preprocessor_path is None:
        preprocessor_path = model_path

    if "deqa" 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(preprocessor_path, use_fast=False)
            print("Loading mPLUG-Owl2 from base model...")
            model = MPLUGOwl2LlamaForCausalLM.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 mPLUG-Owl2 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",
                )
                print(non_lora_trainables.keys())
            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[17:] if k.startswith("base_model.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 mPLUG-Owl2 from base model...")
            tokenizer = AutoTokenizer.from_pretrained(preprocessor_path, use_fast=False)
            cfg_pretrained = AutoConfig.from_pretrained(model_path)
            model = MPLUGOwl2LlamaForCausalLM.from_pretrained(
                model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs
            )
        else:
            tokenizer = AutoTokenizer.from_pretrained(preprocessor_path, use_fast=False)
            model = MPLUGOwl2LlamaForCausalLM.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(preprocessor_path, 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:
            tokenizer = AutoTokenizer.from_pretrained(preprocessor_path, use_fast=False)
            model = AutoModelForCausalLM.from_pretrained(
                model_path, low_cpu_mem_usage=True, **kwargs
            )

    # vision_tower = model.get_model().vision_model
    # print(vision_tower.device)
    # vision_tower.to(device=device, dtype=torch.float16)
    image_processor = CLIPImageProcessor.from_pretrained(preprocessor_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