This is a forward function of a model. When doing backward automatically, what would the format be? In `self.prepare_inputs_labels_for_multimodal`, parameters of the model are involved. In `prepare_zigzag_ring_attn_inputs_wvid`, no parameters are involved, it's simply segment the input sequence.
```
class LlavaQwenForCausalLM(Qwen2ForCausalLM_RingAttn, LlavaMetaForCausalLM):
    config_class = LlavaQwenConfig

    def __init__(self, config):
        # super(Qwen2ForCausalLM, self).__init__(config)
        Qwen2ForCausalLM_RingAttn.__init__(self, config)
        config.model_type = "llava_qwen"
        config.rope_scaling = None

        self.model = LlavaQwenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[List[List[int]]] = None,
        return_dict: Optional[bool] = None,
        modalities: Optional[List[str]] = ["image"],
        dpo_forward: Optional[bool] = False,
        cache_position=None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        edit_index=torch.where(input_ids == IMAGE_TOKEN_INDEX)[1].tolist()[-1]
        description_length=input_ids.shape[1] -1 - edit_index
        print(IMAGE_TOKEN_INDEX, input_ids.shape[1], edit_index, description_length)
        print(type(images), images[0].shape, modalities, image_sizes)

        if inputs_embeds is None:
            (input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)

        inputs_embeds, position_ids, labels, attention_mask = prepare_zigzag_ring_attn_inputs_wvid(
            inputs_embeds, 
            position_ids,
            labels, 
            attention_mask,
            inputs_embeds.device.index, #self.accelerator.process_index,
            int(os.getenv('NUM_GPUS')), #self.accelerator.num_processes,
            inputs_embeds.device,
            description_length,
        )

        return super().forward(
                input_ids=input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                inputs_embeds=inputs_embeds,
                labels=labels,
                use_cache=use_cache,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
```