code/xtuner/model/llava_attn.py

# Copyright (c) OpenMMLab. All rights reserved.
import math
import os.path as osp
import warnings
from collections import OrderedDict

import torch
import torch.nn as nn
from accelerate import init_empty_weights
from mmengine import print_log
from mmengine.config import Config, ConfigDict
from mmengine.model import BaseModel
from peft import get_peft_model, prepare_model_for_kbit_training
from transformers import (AddedToken, AutoConfig, CLIPImageProcessor,
                          CLIPVisionModel, LlamaForCausalLM,
                          LlamaTokenizerFast, LlavaConfig,
                          LlavaForConditionalGeneration, LlavaProcessor)
from transformers.integrations import is_deepspeed_zero3_enabled

from xtuner.registry import BUILDER
from xtuner.utils import DEFAULT_IMAGE_TOKEN
from .modules import ProjectorConfig, ProjectorModel, dispatch_modules
from .modules.dispatch import SUPPORT_FLASH1, SUPPORT_FLASH2
from .utils import (LoadWoInit, find_all_linear_names,
                    get_peft_model_state_dict, guess_load_checkpoint,
                    make_inputs_require_grad,
                    prepare_inputs_labels_for_multimodal, traverse_dict)

from .torchscale.model.LongNet import make_longnet_from_name
import torch.nn.functional as F

def convert_state_dict_to_hf(state_dict, mapping):
    new_state_dict = {}
    for key, value in state_dict.items():
        if key.endswith('.inv_freq'):
            continue
        for key_to_modify, new_key in mapping.items():
            if key_to_modify in key:
                key = key.replace(key_to_modify, new_key)
        new_state_dict[key] = value
    return new_state_dict

class AdaptiveAvgPool1dLayer(nn.Module):
    def __init__(self, output_size):
        super(AdaptiveAvgPool1dLayer, self).__init__()
        self.output_size = output_size

    def forward(self, x):
        return F.adaptive_avg_pool1d(x, self.output_size)
    
class LLaVAModel_Attn(BaseModel):

    def __init__(self,
                 llm,
                 visual_encoder,
                 freeze_llm=True,
                 freeze_visual_encoder=True,
                 visual_select_layer=-2,
                 pretrained_pth=None,
                 projector_depth=2,
                 llm_lora=None,
                 visual_encoder_lora=None,
                 use_activation_checkpointing=True,
                 max_position_embeddings=None,
                 hidden_size=512,
                 train_stage='2',
                 enable_long_net=True):
        super().__init__()
        self.enable_long_net = enable_long_net
        if enable_long_net:
            print('enable long net')
        else:
            print('disable long net')
        self.freeze_llm = freeze_llm
        self.freeze_visual_encoder = freeze_visual_encoder
        if train_stage == '1':
            print('train_stage == 1')
            self.freeze_llm = True
            self.freeze_long_net = False

        elif train_stage == '2':
            print('train_stage == 2')
            self.freeze_llm = False
            self.freeze_long_net = False

        with LoadWoInit():
            # if isinstance(llm, dict):
            #     llm = self._dispatch_lm_model_cfg(llm, max_position_embeddings)

            self.llm = self._build_from_cfg_or_module(llm)
            self.visual_encoder = self._build_from_cfg_or_module(
                visual_encoder)
            

        self.encoder_name = "LongNet_{}_layers_{}_dim".format(2, 512)
        self.LongNet_encoder = make_longnet_from_name(self.encoder_name) # , drop_path_rate=0.3, dropout=0.3, segment_length=1024
        
        self.llm.config.use_cache = False
        # dispatch_modules(self.llm)

        self.projector_depth = projector_depth

        projector_config = ProjectorConfig(
            visual_hidden_size=hidden_size,
            llm_hidden_size=self.llm.config.hidden_size,
            depth=self.projector_depth)        

        self.projector = ProjectorModel(projector_config).to(
            self.visual_encoder.dtype)
        

        if self.freeze_llm:
            print('freeze_llm')
            self.llm.requires_grad_(False)        
        if self.freeze_long_net:
            print('freeze_long_net')
            self.LongNet_encoder.requires_grad_(False)
            
        if self.freeze_visual_encoder:
            print('freeze_visual_encoder')
            self.visual_encoder.requires_grad_(False)
        

        if use_activation_checkpointing:
            # For backward compatibility
            if hasattr(self.llm, 'enable_input_require_grads'):
                self.llm.enable_input_require_grads()
            else:
                self.llm.get_input_embeddings().register_forward_hook(
                    make_inputs_require_grad)
                
            # if hasattr(self.visual_encoder, 'enable_input_require_grads'):
            #     self.visual_encoder.enable_input_require_grads()
            # else:
            #     self.visual_encoder.get_input_embeddings(
            #     ).register_forward_hook(make_inputs_require_grad)

            self.projector.enable_input_require_grads()
            # self.LongNet_encoder.enable_input_require_grads()

            # enable gradient (activation) checkpointing for memory efficiency
            self.gradient_checkpointing_enable()

        self.use_llm_lora =  None
        self.use_visual_encoder_lora =  None

        if self.use_llm_lora:
            self._prepare_llm_for_lora(llm_lora, use_activation_checkpointing)
        # if self.use_visual_encoder_lora:
        #     self._prepare_visual_encoder_for_lora(
        #         visual_encoder_lora, use_activation_checkpointing)

        if pretrained_pth is not None:
            pretrained_state_dict = guess_load_checkpoint(pretrained_pth)

            self.load_state_dict(pretrained_state_dict, strict=False)
            print_log(f'Load pretrained weight from {pretrained_pth}',
                      'current')

        self.visual_select_layer = visual_select_layer

        self._is_init = True

        self.is_first_iter = True

    def _parse_lora_config(self, lora_config):
        if isinstance(lora_config, dict) or isinstance(
                lora_config, Config) or isinstance(lora_config, ConfigDict):
            lora_config = BUILDER.build(lora_config)
        return lora_config

    def _prepare_llm_for_lora(self,
                              lora_config,
                              use_activation_checkpointing=True):
        lora_config = self._parse_lora_config(lora_config)
        self.llm = prepare_model_for_kbit_training(
            self.llm, use_activation_checkpointing)
        if lora_config.target_modules is None:
            modules = find_all_linear_names(self.llm)
            lora_config.target_modules = modules
        self.llm = get_peft_model(self.llm, lora_config)

    def _prepare_visual_encoder_for_lora(self,
                                         lora_config,
                                         use_activation_checkpointing=True):
        lora_config = self._parse_lora_config(lora_config)
        if lora_config.target_modules is None:
            modules = find_all_linear_names(self.visual_encoder)
            lora_config.target_modules = modules
        self.visual_encoder = get_peft_model(self.visual_encoder, lora_config)

    def gradient_checkpointing_enable(self):
        self.activation_checkpointing_enable()

    def activation_checkpointing_enable(self):
        self.llm.gradient_checkpointing_enable()
        # self.visual_encoder.gradient_checkpointing_enable()
        self.projector.gradient_checkpointing_enable()

    def gradient_checkpointing_disable(self):
        self.activation_checkpointing_disable()

    def activation_checkpointing_disable(self):
        self.llm.gradient_checkpointing_disable()
        # self.visual_encoder.gradient_checkpointing_disable()
        self.projector.gradient_checkpointing_disable()

    def init_weights(self):
        pass

    def state_dict(self, *args, **kwargs):
        state_dict = super().state_dict(*args, **kwargs)
        to_return = OrderedDict()
        # Step 1. visual_encoder
        if self.use_visual_encoder_lora:
            to_return.update(
                get_peft_model_state_dict(
                    self.visual_encoder, state_dict=state_dict))
        elif not self.freeze_visual_encoder:
            to_return.update({
                k: v
                for k, v in state_dict.items() if 'visual_encoder.' in k
            })
        # Step 2. LLM
        if self.use_llm_lora:
            to_return.update(
                get_peft_model_state_dict(self.llm, state_dict=state_dict))
        elif not self.freeze_llm:
            to_return.update(
                {k: v
                 for k, v in state_dict.items() if 'llm.' in k})
        # Step 3. Projector
        to_return.update(
            {k: v
             for k, v in state_dict.items() if 'projector.' in k})
        
        # Step 4. LongNet_encoder
        to_return.update(
            {k: v
             for k, v in state_dict.items() if 'LongNet_encoder.' in k})
        return to_return

    @staticmethod
    def _prepare_for_long_context_training(cfg, llm_cfg,
                                           max_position_embeddings):

        orig_rope_scaling = getattr(llm_cfg, 'rope_scaling', None)
        if orig_rope_scaling is None:
            orig_rope_scaling = {'factor': 1}

        orig_rope_scaling_factor = orig_rope_scaling[
            'factor'] if 'factor' in orig_rope_scaling.keys() else 1
        orig_ctx_len = getattr(llm_cfg, 'max_position_embeddings', None)
        if orig_ctx_len:
            orig_ctx_len *= orig_rope_scaling_factor
            if max_position_embeddings > orig_ctx_len:
                scaling_factor = float(
                    math.ceil(max_position_embeddings / orig_ctx_len))
                llm_cfg.rope_scaling = {
                    'type': 'linear',
                    'factor': scaling_factor
                }

        # hardcode for internlm2
        llm_cfg.attn_implementation = 'flash_attention_2'
        cfg.config = llm_cfg

        return cfg, llm_cfg

    @staticmethod
    def _prepare_for_flash_attn(cfg, llm_cfg):
        cls_name = type(llm_cfg).__name__
        SUPPORT_SDPA_ATTN = ('LlamaConfig', 'GemmaConfig', 'MistralConfig',
                             'MixtralConfig', 'Qwen2Config', 'Qwen2MoeConfig',
                             'Starcoder2Config', 'Starcoder2Config',
                             'Phi3Config')
        SUPPORT_FLASH_ATTN2 = ('InternLM2Config', 'LlamaConfig', 'GemmaConfig',
                               'MistralConfig', 'MixtralConfig', 'Qwen2Config',
                               'Qwen2MoeConfig', 'Starcoder2Config',
                               'Starcoder2Config', 'Phi3Config')

        torch_dtype = torch.bfloat16 if (
            torch.cuda.is_available() and torch.cuda.is_bf16_supported()) \
            else torch.float16

        if getattr(cfg, 'attn_implementation', None) is not None:
            # Flash Attention 2.0 only supports torch.float16 and
            # torch.bfloat16 dtypes
            if cfg.attn_implementation == 'flash_attention_2':
                cfg.torch_dtype = torch_dtype
        elif SUPPORT_FLASH2 and cls_name in SUPPORT_FLASH_ATTN2:
            cfg.torch_dtype = torch_dtype
            cfg.attn_implementation = 'flash_attention_2'
        elif SUPPORT_FLASH1 and cls_name in SUPPORT_SDPA_ATTN:
            cfg.attn_implementation = 'sdpa'

        return cfg, llm_cfg

    @staticmethod
    def _prepare_for_qlora_zero3(cfg):
        if (not is_deepspeed_zero3_enabled()) or (not hasattr(
                cfg, 'quantization_config')):
            return cfg

        torch_dtype = torch.bfloat16 if (
            torch.cuda.is_available() and torch.cuda.is_bf16_supported()) \
            else torch.float16

        cfg.torch_dtype = torch_dtype
        quantization_config = cfg.quantization_config
        quantization_config.bnb_4bit_compute_dtype = torch_dtype
        quantization_config.bnb_4bit_quant_storage = torch_dtype

        return cfg

    def _dispatch_lm_model_cfg(self, cfg, max_position_embeddings=None):
        cfg = self._prepare_for_qlora_zero3(cfg)
        pretrained_model_name_or_path = cfg.pretrained_model_name_or_path
        llm_cfg = AutoConfig.from_pretrained(
            pretrained_model_name_or_path, trust_remote_code=True)
        cfg, llm_cfg = self._prepare_for_flash_attn(cfg, llm_cfg)
        if max_position_embeddings is not None:
            cfg, llm_cfg = self._prepare_for_long_context_training(
                cfg, llm_cfg, max_position_embeddings)
        return cfg

    def _build_from_cfg_or_module(self, cfg_or_mod):
        if isinstance(cfg_or_mod, nn.Module):
            return cfg_or_mod
        elif isinstance(cfg_or_mod, dict):
            traverse_dict(cfg_or_mod)
            return BUILDER.build(cfg_or_mod)
        else:
            raise NotImplementedError

    def forward(self, data, data_samples=None, mode='loss'):
        if self.is_first_iter:
            # hardcode for qlora DeepSpeed ZeRO3, put buffers and QuantState to
            # device
            # Only required in `LLaVAModel` .
            # We do not need this in `SupervisedFinetune` .
            self.to(data['input_ids'].device)
            self.is_first_iter = False
        
        # data_dict['pixel_values']=[[pixel_values of img1], [pixel_values of img2], ...]
        if 'pixel_values' in data:

            # multi-image
            # # data['pixel_values'].shape: (img_num, 3, 336, 336)
            # visual_outputs = self.visual_encoder(
            #     data['pixel_values'].to(self.visual_encoder.dtype),
            #     output_hidden_states=True)
            
            # # visual_outputs.hidden_states[self.visual_select_layer][:, 1:].shape: (img_num, 576, 1024)

            # long_net_output = self.LongNet_encoder(src_tokens=None, token_embeddings=visual_outputs.hidden_states[self.visual_select_layer][:, 1:].permute(1, 0, 2))["encoder_out"] # shape: (576, img_num, 1024)
            # feat_to_proj = long_net_output.permute(1, 0, 2) # shape: (img_num, 576, 1024)
            # feat_to_proj = torch.mean(feat_to_proj, dim=0, keepdim=True) # shape: (1, 576, 1024)

            # wsi
            # chunks = torch.chunk(data['pixel_values'], chunks=5000, dim=1)
            # # 对每个 chunk 进行处理
            # processed_chunks = [torch.mean(chunk, dim=1, keepdim=True) for chunk in chunks]
            # # 合并处理后的 chunks
            # merged_tensor = torch.cat(processed_chunks, dim=1)
            # feat_to_proj = merged_tensor.to(self.visual_encoder.dtype)

            # print(data['pixel_values'].shape)
            # print('------------------------------self.visual_encoder.dtype--------------------------------------', self.visual_encoder.dtype)
            feat_to_proj = data['pixel_values'].to(self.visual_encoder.dtype) # torch.Size([1, img_num, 768])
            if self.enable_long_net:
                long_net_output = self.LongNet_encoder(src_tokens=None, token_embeddings=feat_to_proj.permute(1, 0, 2))["encoder_out"] # shape: (576, img_num, 1024)
                feat_to_proj = long_net_output.permute(1, 0, 2) # shape: [1, img_num, 768]
            
            # print('to projector')
            # print('*'*30)555pp0--00[p\]
            # print('feat_to_proj')
            # print(feat_to_proj.shape)
            
            pixel_values = self.projector(feat_to_proj.to(self.visual_encoder.dtype))



            data['pixel_values'] = pixel_values # shape: [1, 576, 4096]
            data = prepare_inputs_labels_for_multimodal(llm=self.llm, **data)

        if mode == 'loss':
            return self.compute_loss(data, data_samples)
        elif mode == 'predict':
            return self.predict(data, data_samples)
        elif mode == 'tensor':
            return self._forward(data, data_samples)
        else:
            raise NotImplementedError

    def _forward(self, data, data_samples=None):

        outputs = self.llm(**data)

        return outputs

    def predict(self, data, data_samples=None):
        outputs = self.llm(**data)
        logits_dict = [{'logits': logits} for logits in outputs.logits]
        return logits_dict

    def compute_loss(self, data, data_samples=None):
        outputs = self.llm(**data)
        loss_dict = {'loss': outputs.loss}
        return loss_dict

    def __getattr__(self, name: str):
        try:
            return super().__getattr__(name)
        except AttributeError:
            return getattr(self.llm, name)

    def to_hf(self,
              cfg,
              save_dir,
              fp32=False,
              save_pretrained_kwargs={},
              save_format='xtuner',
              **kwargs):
        if save_format == 'xtuner':
            self.to_xtuner_llava(cfg, save_dir, fp32, save_pretrained_kwargs)
        elif save_format == 'huggingface':
            self.to_huggingface_llava(cfg, save_dir, fp32,
                                      save_pretrained_kwargs)
        elif save_format == 'official':
            self.to_official_llava(cfg, save_dir, fp32, save_pretrained_kwargs)
        else:
            raise NotImplementedError

    def to_xtuner_llava(self,
                        cfg,
                        save_dir,
                        fp32=False,
                        save_pretrained_kwargs={}):
        # LLM
        self.llm.config.use_cache = True
        if not fp32:
            print_log('Convert LLM to float16', 'current')
            self.llm.half()
        if self.use_llm_lora:
            llm_path = osp.join(save_dir, 'llm_adapter')
            print_log(f'Saving LLM adapter to {llm_path}', 'current')
            self.llm.save_pretrained(llm_path, **save_pretrained_kwargs)
        elif not self.freeze_llm:
            llm_path = save_dir
            print_log(f'Saving LLM tokenizer to {llm_path}', 'current')
            tokenizer = BUILDER.build(cfg.tokenizer)
            tokenizer.save_pretrained(llm_path, **save_pretrained_kwargs)
            print_log(f'Saving LLM to {llm_path}', 'current')
            self.llm.save_pretrained(llm_path, **save_pretrained_kwargs)
        self.llm.config.use_cache = False

        # Visual Encoder
        if self.use_visual_encoder_lora:
            visual_encoder_path = osp.join(save_dir, 'visual_encoder_adapter')
            print_log(
                f'Saving visual_encoder adapter to {visual_encoder_path}',
                'current')
            self.visual_encoder.save_pretrained(visual_encoder_path,
                                                **save_pretrained_kwargs)
        elif not self.freeze_visual_encoder:
            visual_encoder_path = osp.join(save_dir, 'visual_encoder')
            print_log(
                'Saving visual_encoder image_processor to'
                f'{visual_encoder_path}', 'current')
            image_processor = BUILDER.build(cfg.image_processor)
            image_processor.save_pretrained(visual_encoder_path,
                                            **save_pretrained_kwargs)
            print_log(f'Saving visual_encoder to {visual_encoder_path}',
                      'current')
            self.visual_encoder.save_pretrained(visual_encoder_path,
                                                **save_pretrained_kwargs)

        # Projector
        projector_path = osp.join(save_dir, 'projector')
        print_log(f'Saving projector to {projector_path}', 'current')
        self.projector.save_pretrained(projector_path,
                                       **save_pretrained_kwargs)

        # LongNet_encoder
        LongNet_encoder_path = osp.join(save_dir, 'LongNet_encoder')
        print_log(f'Saving LongNet_encoder to {LongNet_encoder_path}', 'current')
        self.LongNet_encoder.save_pretrained(LongNet_encoder_path,
                                       **save_pretrained_kwargs)

    def to_huggingface_llava(self,
                             cfg,
                             save_dir,
                             fp32=False,
                             save_pretrained_kwargs={}):

        LLM_MAPPING = {
            'model': 'language_model.model',
            'lm_head': 'language_model.lm_head',
        }
        VIT_MAPPING = {
            'vision_model': 'vision_tower.vision_model',
        }
        PROJECTOR_MAPPING = {
            'model.0': 'multi_modal_projector.linear_1',
            'model.2': 'multi_modal_projector.linear_2',
        }
        LONGNET_MAPPING = {
            'layers.0': 'LongNet_encoder.layers.0',
            'layers.1': 'LongNet_encoder.layers.1',
            'layer_norm': 'LongNet_encoder.layer_norm'
        }

        assert getattr(self.llm, 'hf_quantizer', None) is None, \
            'This conversion format does not support quantized LLM.'

        # get state_dict
        llm = self.llm
        if self.use_llm_lora:
            llm = self.llm.merge_and_unload()
        llm.config.use_cache = True
        if not fp32:
            print_log('Convert LLM to float16', 'current')
            llm.half()

        assert isinstance(llm, LlamaForCausalLM), \
            'This conversion format only supports LlamaForCausalLM.'
        llm_state_dict = llm.state_dict()
        llm_state_dict = convert_state_dict_to_hf(llm_state_dict, LLM_MAPPING)

        need_visual_encoder = (not self.freeze_visual_encoder
                               or self.use_visual_encoder_lora)
        visual_encoder = self.visual_encoder
        if self.use_visual_encoder_lora:
            visual_encoder = self.visual_encoder.merge_and_unload()
        assert isinstance(visual_encoder, CLIPVisionModel),\
            'This conversion format only supports CLIPVisionModel.'
        if need_visual_encoder:
            visual_encoder_state_dict = visual_encoder.state_dict()
            visual_encoder_state_dict = convert_state_dict_to_hf(
                visual_encoder_state_dict, VIT_MAPPING)
        else:
            visual_encoder_state_dict = {}

        projector_state_dict = self.projector.state_dict()
        projector_state_dict = convert_state_dict_to_hf(
            projector_state_dict, PROJECTOR_MAPPING)

        LongNet_encoder_state_dict = self.LongNet_encoder.state_dict()
        LongNet_encoder_state_dict = convert_state_dict_to_hf(
            LongNet_encoder_state_dict, LONGNET_MAPPING)

        state_dict = {
            **projector_state_dict,
            **llm_state_dict,
            **visual_encoder_state_dict,
            **LongNet_encoder_state_dict
        }

        # init model
        text_config = llm.config
        vision_config = visual_encoder.config
        config = LlavaConfig(
            text_config=text_config,
            vision_config=vision_config,
            attn_implementation='eager')

        with init_empty_weights():
            with warnings.catch_warnings():
                warnings.filterwarnings(
                    'ignore', message='.*non-meta.*', category=UserWarning)
                model = LlavaForConditionalGeneration(config)
        model.load_state_dict(state_dict, strict=True, assign=True)

        # processor
        cfg.tokenizer.type = LlamaTokenizerFast.from_pretrained
        tokenizer = BUILDER.build(cfg.tokenizer)

        tokenizer.add_tokens(
            AddedToken(DEFAULT_IMAGE_TOKEN, special=True, normalized=False),
            special_tokens=True)
        tokenizer.add_special_tokens({'pad_token': '<pad>'})

        image_processor = BUILDER.build(cfg.image_processor)
        assert isinstance(image_processor, CLIPImageProcessor),\
            'This conversion format only supports CLIPImageProcessor.'

        processor = LlavaProcessor(
            tokenizer=tokenizer, image_processor=image_processor)

        # Pad to 64 for performance reasons
        pad_shape = 64

        pre_expansion_embeddings = \
            model.language_model.model.embed_tokens.weight.data
        mu = torch.mean(pre_expansion_embeddings, dim=0).float()
        n = pre_expansion_embeddings.size()[0]
        sigma = ((pre_expansion_embeddings - mu).T
                 @ (pre_expansion_embeddings - mu)) / n
        dist = torch.distributions.multivariate_normal.MultivariateNormal(
            mu, covariance_matrix=1e-5 * sigma)

        # We add an image token so we need to resize the model
        ori_vocab_size = config.text_config.vocab_size
        tokenizer_vocab_size = tokenizer.encode('<pad>')[-1]
        added_token = tokenizer_vocab_size - ori_vocab_size

        if added_token > 0:
            model.resize_token_embeddings(ori_vocab_size + added_token,
                                          pad_shape)
            model.language_model.model.embed_tokens.weight.data[
                ori_vocab_size:] = torch.stack(
                    tuple(
                        dist.sample()
                        for _ in range(model.language_model.model.embed_tokens.
                                       weight.data[ori_vocab_size:].shape[0])),
                    dim=0,
                )
            model.language_model.lm_head.weight.data[
                ori_vocab_size:] = torch.stack(
                    tuple(dist.sample()
                          for _ in range(model.language_model.lm_head.weight.
                                         data[ori_vocab_size:].shape[0])),
                    dim=0,
                )
        model.config.image_token_index = tokenizer.encode(
            DEFAULT_IMAGE_TOKEN)[-1]
        model.config.pad_token_id = tokenizer.encode('<pad>')[-1]

        # save
        print_log(f'Saving to {save_dir}', 'current')
        model.save_pretrained(save_dir, **save_pretrained_kwargs)
        processor.save_pretrained(save_dir, **save_pretrained_kwargs)

    def to_official_llava(self,
                          cfg,
                          save_dir,
                          fp32=False,
                          save_pretrained_kwargs={}):

        VIT_MAPPING = {
            'vision_model': 'model.vision_tower.vision_tower.vision_model',
        }
        PROJECTOR_MAPPING = {
            'model.0': 'model.mm_projector.0',
            'model.2': 'model.mm_projector.2',
        }
        LONGNET_MAPPING = {
            'layers.0': 'LongNet_encoder.layers.0',
            'layers.1': 'LongNet_encoder.layers.1',
            'layer_norm': 'LongNet_encoder.layer_norm'
        }

        try:
            from llava.model import LlavaConfig, LlavaLlamaForCausalLM
        except ImportError:
            raise ImportError(
                'Please install llava with '
                '`pip install git+https://github.com/haotian-liu/LLaVA.git '
                '--no-deps`.')

        assert getattr(self.llm, 'hf_quantizer', None) is None, \
            'This conversion format does not support quantized LLM.'

        # get state_dict
        llm = self.llm
        if self.use_llm_lora:
            llm = self.llm.merge_and_unload()
        llm.config.use_cache = True
        if not fp32:
            print_log('Convert LLM to float16', 'current')
            llm.half()

        assert isinstance(llm, LlamaForCausalLM), \
            'This conversion format only supports LlamaForCausalLM.'
        llm_state_dict = llm.state_dict()

        need_visual_encoder = (not self.freeze_visual_encoder
                               or self.use_visual_encoder_lora)
        visual_encoder = self.visual_encoder
        if self.use_visual_encoder_lora:
            visual_encoder = self.visual_encoder.merge_and_unload()
        assert isinstance(visual_encoder, CLIPVisionModel),\
            'This conversion format only supports CLIPVisionModel.'
        if need_visual_encoder:
            visual_encoder_state_dict = visual_encoder.state_dict()
            visual_encoder_state_dict = convert_state_dict_to_hf(
                visual_encoder_state_dict, VIT_MAPPING)
        else:
            visual_encoder_state_dict = {}

        projector_state_dict = self.projector.state_dict()
        projector_state_dict = convert_state_dict_to_hf(
            projector_state_dict, PROJECTOR_MAPPING)

        LongNet_encoder_state_dict = self.LongNet_encoder.state_dict()
        LongNet_encoder_state_dict = convert_state_dict_to_hf(
            LongNet_encoder_state_dict, LONGNET_MAPPING)

        state_dict = {
            **projector_state_dict,
            **llm_state_dict,
            **visual_encoder_state_dict,
            **LongNet_encoder_state_dict
        }

        # init model
        tokenizer = BUILDER.build(cfg.tokenizer)
        image_processor = BUILDER.build(cfg.image_processor)
        assert isinstance(image_processor, CLIPImageProcessor),\
            'This conversion format only supports CLIPImageProcessor.'

        llava_config_dict = llm.config.__dict__.copy()
        llava_config_dict.update(
            dict(
                image_aspect_ratio='pad',
                mm_hidden_size=visual_encoder.config.hidden_size,
                mm_projector_type=f'mlp{self.projector_depth}x_gelu',
                mm_use_im_patch_token=False,
                mm_use_im_start_end=False,
                mm_vision_select_feature='patch',
                mm_vision_select_layer=self.visual_select_layer,
                mm_vision_tower=visual_encoder.config.name_or_path,
                unfreeze_mm_vision_tower=need_visual_encoder,
                model_type='llava',
                use_cache=True,
                use_mm_proj=True))

        llava_config = LlavaConfig(**llava_config_dict)

        with init_empty_weights():
            with warnings.catch_warnings():
                warnings.filterwarnings(
                    'ignore', message='.*non-meta.*', category=UserWarning)
                model = LlavaLlamaForCausalLM(llava_config)

        model.load_state_dict(state_dict, strict=True, assign=True)

        # save
        print_log(f'Saving to {save_dir}', 'current')

        model.save_pretrained(save_dir, **save_pretrained_kwargs)
        image_processor.save_pretrained(save_dir, **save_pretrained_kwargs)
        tokenizer.save_pretrained(save_dir, **save_pretrained_kwargs)