modeling_vila.py

import shutil
import copy
import json
import logging
import math
import os
import os.path
import os.path as osp
import warnings
from abc import ABC
from collections import OrderedDict, defaultdict, deque
from copy import deepcopy
from itertools import chain
from threading import Thread
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import torchvision
from einops import rearrange
from PIL import Image

from transformers import (
    AutoConfig,
    AutoModel,
    AutoProcessor,
    AutoTokenizer,
    GenerationConfig,
    LogitsProcessor,
    PretrainedConfig,
    PreTrainedModel,
    Qwen2Config,
    Qwen2ForCausalLM,
    Qwen2PreTrainedModel,
    TextIteratorStreamer
)
from transformers.modeling_utils import ContextManagers, no_init_weights
from transformers.modeling_outputs import CausalLMOutputWithPast

from .base_projector import MultimodalProjector, MultimodalProjectorConfig
from .builder import build_llm_and_tokenizer
from .configuration_vila import VILAConfig
from .media_encoder import BasicImageEncoder, BasicVideoEncoder
from .siglip_encoder import SiglipVisionTower, SiglipVisionTowerDynamicS2, SiglipVisionTowerS2
from .utils import get_model_config
from .media import extract_media
from .mm_utils import process_image, process_images
from .tokenizer_utils import tokenize_conversation
from .constants import *
from .conversation import default_conversation, SeparatorStyle

# from llava.constants import DEFAULT_IMAGE_TOKEN, IGNORE_INDEX, NUM_EXTRA_TOKENS
# quick hack for remote code
def get_pg_manager():
    return None

def get_model_weights_dtype(model: nn.Module):
    pass


def build_mm_projector(model_type_or_path: str, config: PretrainedConfig) -> PreTrainedModel:
    if model_type_or_path is None:
        return None
    ## load from pretrained model
    if config.resume_path:
        assert os.path.exists(model_type_or_path), f"Resume mm projector path {model_type_or_path} does not exist!"
        return MultimodalProjector.from_pretrained(model_type_or_path, config)
    ## build from scratch
    else:
        mm_projector_cfg = MultimodalProjectorConfig(model_type_or_path)
        mm_projector = MultimodalProjector(mm_projector_cfg, config)
        return mm_projector
    

def build_vision_tower(model_name_or_path: str, config: PretrainedConfig) -> PreTrainedModel:
    ## skip vision tower instantiation
    if model_name_or_path is None:
        return None

    vision_tower_arch = None
    if config.resume_path and "radio" not in model_name_or_path:
        assert os.path.exists(model_name_or_path), f"Resume vision tower path {model_name_or_path} does not exist!"
        vision_tower_cfg = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
        vision_tower_arch = vision_tower_cfg.architectures[0].lower()
    vision_tower_name = vision_tower_arch if vision_tower_arch is not None else model_name_or_path

    use_s2 = getattr(config, "s2", False)
    use_dynamic_s2 = getattr(config, "dynamic_s2", False)

    if "siglip" in vision_tower_name:
        if use_dynamic_s2:
            vision_tower = SiglipVisionTowerDynamicS2(model_name_or_path, config)
        elif use_s2:
            vision_tower = SiglipVisionTowerS2(model_name_or_path, config)
        else:
            vision_tower = SiglipVisionTower(model_name_or_path, config)
    else:
        raise NotImplementedError(f"Unknown vision tower: {model_name_or_path}")

    config.mm_hidden_size = (
        vision_tower.config.hidden_size if not (use_s2 or use_dynamic_s2) else vision_tower.hidden_size
    )
    return vision_tower


class VILAPretrainedModel(PreTrainedModel):
    config_class = VILAConfig
    main_input_name = "input_embeds"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    
    def __init__(self, config: VILAConfig, *args, **kwargs):
        super().__init__(config)
        self.config = config
        cfgs = get_model_config(config)
        if len(cfgs) == 3:
            llm_cfg, vision_tower_cfg, mm_projector_cfg = cfgs
        else:
            raise ValueError("`llm_cfg` `mm_projector_cfg` `vision_tower_cfg` not found in the config.")
        
        # loading on cpu by default
        device_map = kwargs.get("device_map", "cpu")
        self.mm_projector = build_mm_projector(mm_projector_cfg, config)
        self.vision_tower = build_vision_tower(vision_tower_cfg, config)
        if "auto" in device_map or "cuda" in device_map:
            self.mm_projector = self.mm_projector.cuda()
            self.vision_tower = self.vision_tower.cuda() 
        # set device_map auto can autoamtically shard llm to different devices
        self.llm, self.tokenizer = self.init_llm(llm_cfg, config, device_map=device_map)
        
        self.encoders = {
            "image": BasicImageEncoder(self),
            "video": BasicVideoEncoder(self)
        }
        
        self.post_config()
        self.is_loaded = True

        assert (
            self.llm is not None or self.vision_tower is not None or self.mm_projector is not None
        ), "At least one of the components must be instantiated."

    @classmethod
    def convert_vila_dev_ckpt_to_remote(self, model_path: str, output_dir:str = None, *model_args, **kwargs):
        # assert type(self) == VILAForCasualLM, "This method is only available for VILAForCasualLM."
        from huggingface_hub import HfApi, snapshot_download

        if os.path.isdir(model_path):
            model_path = model_path
        api = HfApi()
        if api.repo_exists(model_path):
            model_path = snapshot_download(model_path, local_dir=output_dir)
            print("downloading HF model to", model_path)
        
        cfg_path = os.path.join(model_path, "config.json")
        config = json.load(open(cfg_path))
        config["version"] = "2.0" # nvila tag
        config["architectures"] = ["VILAForCasualLM"]
        config["auto_map"] = {
            "AutoConfig": "modeling_vila.VILAConfig",
            "AutoModel": "modeling_vila.VILAForCasualLM",
            "AutoModelForCausalLM": "modeling_vila.VILAForCasualLM"
        }
        config["model_type"] = "vila"
        json.dump(config, open(cfg_path, "w"), indent=2)
        self.copy_remote_py_files(model_path)
        
    @classmethod
    def copy_remote_py_files(cls, output_dir):
        ## copy .py and REAMDE for next loading remote code
        current_file_path = os.path.abspath(__file__)
        current_folder = os.path.dirname(current_file_path)
        for file_name in os.listdir(current_folder):
            if file_name.endswith(".py"):
                full_file_name = os.path.join(current_folder, file_name)
                if os.path.isfile(full_file_name):
                    shutil.copy(full_file_name, output_dir)
                    print("[HF remote code] copying", full_file_name, "to", output_dir)

    def save_pretrained(self, output_dir, state_dict=None):
        if state_dict is None:
            # other wise fetch from deepspeed
            # state_dict = accelerator.get_state_dict(is_deepspeed_enabled)
            state_dict = self.state_dict()

        if getattr(self, "tokenizer", None):
            self.tokenizer.save_pretrained(osp.join(output_dir, "llm"))

        if self.get_llm():
            print(f"saving llm to {osp.join(output_dir, 'llm')}")
            self.llm.config._name_or_path = osp.join(output_dir, "llm")
            llm_state_dict = OrderedDict({k.split("llm.")[-1]: v for k, v in state_dict.items() if "llm" in k})
            self.llm.save_pretrained(os.path.join(output_dir, "llm"), state_dict=llm_state_dict)
            self.config.llm_cfg = self.llm.config

        if self.get_vision_tower():
            print(f"saving vision_tower to {osp.join(output_dir, 'vision_tower')}")
            self.vision_tower.config._name_or_path = osp.join(output_dir, "vision_tower")
            vision_tower_state_dict = OrderedDict(
                {k.split("vision_tower.vision_tower.")[-1]: v for k, v in state_dict.items() if "vision_tower" in k}
            )
            self.vision_tower.vision_tower.save_pretrained(
                os.path.join(output_dir, "vision_tower"),
                state_dict=vision_tower_state_dict,
            )
            self.vision_tower.image_processor.save_pretrained(os.path.join(output_dir, "vision_tower"))
            self.config.vision_tower_cfg = self.vision_tower.config
            if hasattr(self.config.vision_tower_cfg, "auto_map"):
                if "radio" not in self.get_vision_tower().__class__.__name__.lower():
                    delattr(self.config.vision_tower_cfg, "auto_map")

        if self.get_mm_projector():
            print(f"saving mm_projector to {osp.join(output_dir, 'mm_projector')}")
            self.mm_projector.config._name_or_path = osp.join(output_dir, "mm_projector")
            mm_projector_state_dict = OrderedDict(
                {k.split("mm_projector.")[-1]: v for k, v in state_dict.items() if "mm_projector" in k}
            )
            self.mm_projector.save_pretrained(
                os.path.join(output_dir, "mm_projector"),
                state_dict=mm_projector_state_dict,
            )
            self.config.mm_projector_cfg = self.mm_projector.config
        
        ## update and save top-level config
        self.config._name_or_path = output_dir
        self.config.architectures = [self.__class__.__name__]
        self.config.save_pretrained(output_dir)
        
        ## copy .py and REAMDE for next loading remote code
        self.copy_remote_py_files(output_dir)

        

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[str] = None,
        *model_args,
        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        ignore_mismatched_sizes: bool = False,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        use_safetensors: Optional[bool] = None,
        weights_only: bool = True,
        **kwargs,
    ):
        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
        return cls._from_config(config, **kwargs)

    def init_llm(self, llm_config, config, *args, **kwargs):
        self.llm, self.tokenizer = build_llm_and_tokenizer(llm_config, config, *args, **kwargs)
        # hard coded for NVILA
        # variables for XGrammar
        # print("DEBUG", len(self.tokenizer.added_tokens_encoder.keys()), self.tokenizer.added_tokens_encoder.keys())
        NUM_EXTRA_TOKENS = len(self.tokenizer.added_tokens_encoder.keys())
        
        # TODO: SENTINEL_TOKEN is not added, need to check with Zhijian
        self.vocab_size = self.tokenizer.vocab_size + NUM_EXTRA_TOKENS
        # XGrammar tokenizer and grammar compiler
        # lazy init only when specified json output during inference
        self.grammar_compiler = None

        self.llm.resize_token_embeddings(len(self.tokenizer))
        return self.llm, self.tokenizer

    def post_config(self):
        ######################################################################
        # TODO: need to check dtype with jason
        self.llm = self.llm.to(torch.float16)
        self.mm_projector = self.mm_projector.to(torch.float16)
        self.vision_tower = self.vision_tower.to(torch.float16)
        ######################################################################
        self.training = self.llm.training
        ## configuration
        if getattr(self.config, "llm_cfg", None) is None:
            self.config.llm_cfg = self.llm.config
        if getattr(self.config, "vision_tower_cfg", None) is None:
            self.config.vision_tower_cfg = self.vision_tower.config
        if getattr(self.config, "mm_projector_cfg", None) is None:
            self.config.mm_projector_cfg = self.mm_projector.config

    def get_llm(self):
        llm = getattr(self, "llm", None)
        if type(llm) is list:
            llm = llm[0]
        return llm

    def get_lm_head(self):
        lm_head = getattr(self.get_llm(), "lm_head", None)
        return lm_head

    def get_vision_tower(self):
        vision_tower = getattr(self, "vision_tower", None)
        if type(vision_tower) is list:
            vision_tower = vision_tower[0]
        return vision_tower

    def get_mm_projector(self):
        mm_projector = getattr(self, "mm_projector", None)
        if type(mm_projector) is list:
            mm_projector = mm_projector[0]
        return mm_projector

    def freezed_module_patch(self):
        """
        Huggingface will call model.train() at each training_step. To ensure the expected behaviors for modules like dropout, batchnorm, etc., we need to call model.eval() for the freezed modules.
        """
        if self.training:
            if self.get_llm() and not getattr(self.config, "tune_language_model", False):
                pass
                # logging.warning("Caution: Your LLM is currently in training mode, ensuring accurate gradient computation. Please be vigilant, particularly regarding BatchNorm and Dropout operations.")
            if self.get_vision_tower() and not getattr(self.config, "tune_vision_tower", False):
                self.get_vision_tower().eval()
            if self.get_mm_projector() and not getattr(self.config, "tune_mm_projector", False):
                self.get_mm_projector().eval()
    
class VILAForCasualLM(VILAPretrainedModel):
    def __init__(self, config: VILAConfig, *args, **kwargs):
        super().__init__(config, *args, **kwargs)
    
    def merge_features_for_dynamic_s2(self, image_features, block_sizes):
        scales = self.get_vision_tower().scales
        resize_output_to_scale_idx = self.get_vision_tower().resize_output_to_scale_idx

        image_features_each_image = []
        new_block_sizes = []
        block_cnt = 0
        for block_size_each_image in block_sizes:
            if block_size_each_image is None:
                cur_features = image_features[block_cnt : block_cnt + 1]
                cur_features = rearrange(cur_features, "1 (h w) c -> 1 c h w", h=int(cur_features.shape[1] ** 0.5))
                cur_features = cur_features.repeat(1, len(scales), 1, 1)
                image_features_each_image.append(cur_features)
                new_block_sizes.append((1, 1))
                block_cnt += 1
            else:
                cur_features_each_scale = []
                for scale in scales[:-1]:
                    num_blocks_this_scale = (scale // scales[0]) ** 2
                    cur_features_each_scale.append(
                        self.merge_chessboard(
                            image_features[block_cnt : block_cnt + num_blocks_this_scale],
                            num_split_h=scale // scales[0],
                            num_split_w=scale // scales[0],
                        )
                    )  # 1 * C * H * W
                    block_cnt += num_blocks_this_scale
                num_blocks_last_scale = block_size_each_image[0] * block_size_each_image[1]
                cur_features_each_scale.append(
                    self.merge_chessboard(
                        image_features[block_cnt : block_cnt + num_blocks_last_scale],
                        num_split_h=block_size_each_image[0],
                        num_split_w=block_size_each_image[1],
                    )
                )  # 1 * C * H * W
                block_cnt += num_blocks_last_scale

                # resize and concat features from different scales
                output_size = cur_features_each_scale[resize_output_to_scale_idx].shape[-2:]
                cur_features = torch.cat(
                    [
                        F.interpolate(cur_features_each_scale[i].to(torch.float32), size=output_size, mode="area").to(
                            cur_features_each_scale[i].dtype
                        )
                        for i in range(len(cur_features_each_scale))
                    ],
                    dim=1,
                )
                # cur_features = rearrange(cur_features, "1 c h w -> (h w) c")

                image_features_each_image.append(cur_features)

                if resize_output_to_scale_idx == len(scales) - 1 or resize_output_to_scale_idx == -1:
                    new_block_sizes.append(block_size_each_image)
                else:
                    new_block_sizes.append(
                        (
                            scales[resize_output_to_scale_idx] // scales[0],
                            scales[resize_output_to_scale_idx] // scales[0],
                        )
                    )

        assert block_cnt == len(image_features)

        return image_features_each_image, new_block_sizes

    def encode_images(self, images, block_sizes: Optional[Optional[Tuple[int, ...]]] = None):
        if block_sizes is None:
            block_sizes = [None] * len(images)
        if getattr(self.config, "dynamic_s2", False):
            image_features = self.get_vision_tower()(images)
            image_features, new_block_sizes = self.merge_features_for_dynamic_s2(image_features, block_sizes)
            
            image_features = [
                self.split_chessboard(x, block_size[0], block_size[1])
                for x, block_size in zip(image_features, new_block_sizes)
            ]  # list of B * C * H * W tensors
            image_features = torch.cat(
                [rearrange(x, "b c h w -> b (h w) c") for x in image_features], dim=0
            )  # B * N * C
            image_features = self.get_mm_projector()(image_features)
            image_features = list(
                image_features.split([block_size[0] * block_size[1] for block_size in new_block_sizes], dim=0)
            )
            image_features = [
                self.merge_chessboard(x, block_size[0], block_size[1])
                for x, block_size in zip(image_features, new_block_sizes)
            ]  # list of 1 * C * H * W tensors
            image_features = [rearrange(x, "1 c h w -> (h w) c") for x in image_features]  # list of N * C tensors
            if all([feature.shape[0] == image_features[0].shape[0] for feature in image_features]):
                image_features = torch.stack(image_features, dim=0)
        else:
            image_features = self.get_vision_tower()(images)
            image_features = self.get_mm_projector()(image_features)
        return image_features

    def _embed(
        self,
        input_ids: torch.Tensor,
        media: Dict[str, List[torch.Tensor]],
        media_config: Dict[str, Dict[str, Any]],
        labels: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        labels = labels if labels is not None else torch.full_like(input_ids, IGNORE_INDEX)
        attention_mask = attention_mask if attention_mask is not None else torch.ones_like(input_ids, dtype=torch.bool)

        # PROCESS_GROUP_MANAGER = get_pg_manager()
        PROCESS_GROUP_MANAGER = None
        if PROCESS_GROUP_MANAGER is not None:
            for name in media:
                self.encoders[name].end_tokens = None

        # Extract text and media embeddings
        text_embeds = self.llm.model.embed_tokens(input_ids)
        media_embeds = self.__embed_media_tokens(media, media_config)

        # This is a workaround to make sure the dummy embeddings are consumed
        while media_embeds.get("dummy"):
            dummy_embed = media_embeds["dummy"].popleft()
            text_embeds += torch.sum(dummy_embed) * 0

        # Remove padding
        batch_size = labels.shape[0]
        text_embeds = [text_embeds[k][attention_mask[k]] for k in range(batch_size)]
        labels = [labels[k][attention_mask[k]] for k in range(batch_size)]

        # Build inverse mapping from token ID to media name
        media_tokens = {}
        for name, token_id in self.tokenizer.media_token_ids.items():
            media_tokens[token_id] = name

        # Fuse text and media embeddings
        inputs_m, labels_m = [], []
        for k in range(batch_size):
            inputs_mk, labels_mk = [], []
            pos = 0
            while pos < len(labels[k]):
                if input_ids[k][pos].item() in media_tokens:
                    end = pos + 1
                    name = media_tokens[input_ids[k][pos].item()]
                    input = media_embeds[name].popleft()
                    label = torch.full([input.shape[0]], IGNORE_INDEX, device=labels[k].device, dtype=labels[k].dtype)
                else:
                    end = pos
                    while end < len(labels[k]) and input_ids[k][end].item() not in media_tokens:
                        end += 1
                    input = text_embeds[k][pos:end]
                    label = labels[k][pos:end]
                inputs_mk.append(input)
                labels_mk.append(label)
                pos = end
            inputs_m.append(torch.cat(inputs_mk, dim=0))
            labels_m.append(torch.cat(labels_mk, dim=0))
        inputs, labels = inputs_m, labels_m

        # Check if all media embeddings are consumed
        for name in media_embeds:
            if media_embeds[name]:
                raise ValueError(f"Not all {name} embeddings are consumed!")

        # Truncate sequences to `model_max_length` as media embeddings are inserted
        inputs, labels = self.__truncate_sequence(inputs, labels)

        # Pad sequences to the longest one in the batch
        return self.__batchify_sequence(inputs, labels)

    def __embed_media_tokens(
        self,
        media: Dict[str, List[torch.Tensor]],
        media_config: Dict[str, Dict[str, Any]],
    ) -> Dict[str, List[torch.Tensor]]:
        embeds = defaultdict(deque)
        for name in media:
            if self.training:
                # Gather metainfo of media objects from all ranks
                info = [{"shape": tensor.shape, "dtype": tensor.dtype} for tensor in media.get(name, [])]
                infos = list(chain(*distributed.all_gather(info)))

                # The entire batch does not contain any media objects of this type.
                if not infos:
                    continue

                # Create a dummy tensor to ensure the encoder is called, otherwise the training will hang.
                if media.get(name) is None or len(media[name]) == 0:
                    dummy = torch.zeros(infos[0]["shape"], dtype=infos[0]["dtype"], device=self.device)
                    embeds["dummy"].extend(self.encoders[name]([dummy], media_config[name]))
                    continue
            embeds[name] = deque(self.encoders[name](media[name], media_config[name]))
        return embeds

    def __truncate_sequence(
        self, inputs: List[torch.Tensor], labels: List[torch.Tensor]
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        if self.training and any(len(input) > self.tokenizer.model_max_length for input in inputs):
            warnings.warn(f"Truncating sequences to `model_max_length` ({self.tokenizer.model_max_length}).")
            inputs = [input[: self.tokenizer.model_max_length] for input in inputs]
            labels = [label[: self.tokenizer.model_max_length] for label in labels]
        return inputs, labels

    def __batchify_sequence(
        self, inputs: List[torch.Tensor], labels: List[torch.Tensor]
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        batch_size = len(inputs)
        device = inputs[0].device
        hidden_size = inputs[0].shape[1]
        max_length = max(inputs[k].shape[0] for k in range(batch_size))
        attention_mask = torch.ones((batch_size, max_length), dtype=torch.bool, device=device)

        inputs_p, labels_p = [], []
        for k in range(batch_size):
            size_pk = max_length - inputs[k].shape[0]
            inputs_pk = torch.zeros((size_pk, hidden_size), dtype=inputs[k].dtype, device=device)
            labels_pk = torch.full((size_pk,), IGNORE_INDEX, dtype=labels[k].dtype, device=device)
            if self.tokenizer.padding_side == "right":
                attention_mask[k, inputs[k].shape[0] :] = False
                inputs_pk = torch.cat([inputs[k], inputs_pk], dim=0)
                labels_pk = torch.cat([labels[k], labels_pk], dim=0)
            else:
                attention_mask[k, : -inputs[k].shape[0]] = False
                inputs_pk = torch.cat([inputs_pk, inputs[k]], dim=0)
                labels_pk = torch.cat([labels_pk, labels[k]], dim=0)
            inputs_p.append(inputs_pk)
            labels_p.append(labels_pk)

        inputs = torch.stack(inputs_p, dim=0)
        labels = torch.stack(labels_p, dim=0)
        return inputs, labels, attention_mask

    def repack_multimodal_data(self, inputs_embeds, attention_mask, position_ids, labels):
        # Handle sequence parallelism
        PROCESS_GROUP_MANAGER = get_pg_manager()

        # We do re-sharding instead of packing here to ensure the sequence length is the same across all ranks.
        if PROCESS_GROUP_MANAGER is not None:
            sp_degree = PROCESS_GROUP_MANAGER.sp_degree
            sp_rank = PROCESS_GROUP_MANAGER.sp_rank
            sp_group = PROCESS_GROUP_MANAGER.sp_pg
            ring_degree = PROCESS_GROUP_MANAGER.ring_degree
            ring_rank = PROCESS_GROUP_MANAGER.ring_rank
            ring_type = PROCESS_GROUP_MANAGER.ring_type
            ulysses_degree = PROCESS_GROUP_MANAGER.ulysses_degree
            ulysses_rank = PROCESS_GROUP_MANAGER.ulysses_rank

            bs, shard_seqlen = position_ids.shape
            sp_seq_len = [torch.zeros(1, dtype=torch.int64, device=position_ids.device) for _ in range(sp_degree)]
            dist.all_gather(sp_seq_len, torch.tensor(shard_seqlen, device=position_ids.device), group=sp_group)
            sp_seq_len_cat = torch.cat(sp_seq_len, dim=0)

            if sp_rank == 0:
                original_start_id = 0
            else:
                original_start_id = torch.sum(sp_seq_len_cat[:sp_rank]).item()
            original_end_id = torch.sum(sp_seq_len_cat[: sp_rank + 1]).item()

            # Gather attention_mask, position_ids, labels and input_embeds
            all_inputs_embeds = torch.zeros(
                bs,
                torch.sum(sp_seq_len_cat),
                inputs_embeds.shape[-1],
                dtype=inputs_embeds.dtype,
                device=inputs_embeds.device,
            ).contiguous()
            all_inputs_embeds[:, original_start_id:original_end_id, :] += inputs_embeds
            dist.barrier(group=sp_group)
            dist.all_reduce(all_inputs_embeds, group=sp_group)
            dist.barrier(group=sp_group)

            attention_mask_list = [
                torch.zeros((bs, sp_seq_len[i]), dtype=attention_mask.dtype, device=attention_mask.device)
                for i in range(sp_degree)
            ]
            position_ids_list = [
                torch.zeros((bs, sp_seq_len[i]), dtype=position_ids.dtype, device=position_ids.device)
                for i in range(sp_degree)
            ]
            labels_list = [
                torch.zeros((bs, sp_seq_len[i]), dtype=labels.dtype, device=labels.device) for i in range(sp_degree)
            ]

            dist.all_gather(attention_mask_list, attention_mask, group=sp_group)
            dist.all_gather(position_ids_list, position_ids, group=sp_group)
            dist.all_gather(labels_list, labels, group=sp_group)

            effective_seqlen_list = [attention_mask_list[i].sum(dim=-1) for i in range(sp_degree)]
            effective_seqlen = torch.stack(effective_seqlen_list, dim=-1)
            effective_seqlen_batch_list = torch.unbind(effective_seqlen, dim=0)

            global_attention_mask_list = []
            global_position_ids_list = []
            global_labels_list = []
            global_inputs_embeds_list = []
            for i in range(bs):
                global_attention_mask_batch_list = []
                global_position_ids_batch_list = []
                global_labels_batch_list = []
                global_inputs_embeds_batch_list = []
                for j in range(sp_degree):
                    eff_len = effective_seqlen_batch_list[i][j]
                    prev_len = torch.sum(sp_seq_len_cat[:j]).item() if j > 0 else 0

                    global_attention_mask_batch_list.append(attention_mask_list[j][i, :eff_len])
                    global_position_ids_batch_list.append(position_ids_list[j][i, :eff_len])
                    global_labels_batch_list.append(labels_list[j][i, :eff_len])
                    global_inputs_embeds_batch_list.append(all_inputs_embeds[i, prev_len : prev_len + eff_len, :])
                global_attention_mask_list.append(torch.cat(global_attention_mask_batch_list, dim=0))
                global_position_ids_list.append(torch.cat(global_position_ids_batch_list, dim=0))
                global_labels_list.append(torch.cat(global_labels_batch_list, dim=0))
                global_inputs_embeds_list.append(torch.cat(global_inputs_embeds_batch_list, dim=0))

                global_attention_mask = torch.nn.utils.rnn.pad_sequence(
                    global_attention_mask_list, batch_first=True, padding_value=False
                )
                global_position_ids = torch.nn.utils.rnn.pad_sequence(
                    global_position_ids_list, batch_first=True, padding_value=-1
                )
                global_labels = torch.nn.utils.rnn.pad_sequence(
                    global_labels_list, batch_first=True, padding_value=IGNORE_INDEX
                )
                global_inputs_embeds = torch.nn.utils.rnn.pad_sequence(
                    global_inputs_embeds_list, batch_first=True, padding_value=0
                )

            # Re-shard the inputs
            if ring_degree > 1:
                total_effective_seqlen = torch.sum(effective_seqlen, dim=1)
                new_seqlen_per_rank = total_effective_seqlen // sp_degree
                assert torch.all(
                    total_effective_seqlen % sp_degree == 0
                ), "total_effective_seqlen must be divisible by sp_degree"

                max_new_seqlen = torch.max(new_seqlen_per_rank).item()

                new_attention_mask = torch.zeros(
                    (bs, max_new_seqlen), dtype=global_attention_mask.dtype, device=global_attention_mask.device
                )
                new_position_ids = torch.zeros(
                    (bs, max_new_seqlen), dtype=global_position_ids.dtype, device=global_position_ids.device
                )
                new_labels = torch.full(
                    (bs, max_new_seqlen), IGNORE_INDEX, dtype=global_labels.dtype, device=global_labels.device
                )
                new_inputs_embeds = torch.zeros(
                    (bs, max_new_seqlen, global_inputs_embeds.shape[-1]),
                    dtype=global_inputs_embeds.dtype,
                    device=global_inputs_embeds.device,
                )

                if ring_type == "ring_varlen":
                    for i in range(bs):
                        start_idx = new_seqlen_per_rank[i] * sp_rank
                        end_idx = start_idx + new_seqlen_per_rank[i]
                        new_attention_mask[i, : new_seqlen_per_rank[i]] = global_attention_mask[i, start_idx:end_idx]
                        new_position_ids[i, : new_seqlen_per_rank[i]] = global_position_ids[i, start_idx:end_idx]
                        new_labels[i, : new_seqlen_per_rank[i]] = global_labels[i, start_idx:end_idx]
                        new_inputs_embeds[i, : new_seqlen_per_rank[i], :] = global_inputs_embeds[
                            i, start_idx:end_idx, :
                        ]
                elif ring_type == "zigzag_ring_varlen":
                    chunk_size = total_effective_seqlen // (2 * sp_degree)
                    for i in range(bs):
                        # Zigzag pattern indices
                        if sp_degree == ring_degree:
                            forward_rank_idx = sp_rank
                            backward_rank_idx = 2 * sp_degree - sp_rank - 1
                        else:
                            ulysses_offset = ulysses_rank * ring_degree * 2
                            forward_rank_idx = ring_rank + ulysses_offset
                            backward_rank_idx = sp_degree - ring_rank - 1 + ulysses_offset

                        # Calculate start and end indices for the forward and backward zigzag
                        start_idx_fwd = forward_rank_idx * chunk_size[i]
                        end_idx_fwd = start_idx_fwd + chunk_size[i]

                        start_idx_bwd = backward_rank_idx * chunk_size[i]
                        end_idx_bwd = start_idx_bwd + chunk_size[i]

                        # Fill new tensors with zigzag data
                        new_attention_mask[i, : chunk_size[i]] = global_attention_mask[i, start_idx_fwd:end_idx_fwd]
                        new_attention_mask[i, chunk_size[i] : 2 * chunk_size[i]] = global_attention_mask[
                            i, start_idx_bwd:end_idx_bwd
                        ]

                        new_position_ids[i, : chunk_size[i]] = global_position_ids[i, start_idx_fwd:end_idx_fwd]
                        new_position_ids[i, chunk_size[i] : 2 * chunk_size[i]] = global_position_ids[
                            i, start_idx_bwd:end_idx_bwd
                        ]

                        new_labels[i, : chunk_size[i]] = global_labels[i, start_idx_fwd:end_idx_fwd]
                        new_labels[i, chunk_size[i] : 2 * chunk_size[i]] = global_labels[i, start_idx_bwd:end_idx_bwd]

                        new_inputs_embeds[i, : chunk_size[i], :] = global_inputs_embeds[i, start_idx_fwd:end_idx_fwd, :]
                        new_inputs_embeds[i, chunk_size[i] : 2 * chunk_size[i], :] = global_inputs_embeds[
                            i, start_idx_bwd:end_idx_bwd, :
                        ]
                else:
                    raise ValueError(f"Invalid ring_type: {ring_type}")
            else:
                global_seq_len = global_attention_mask.shape[-1]
                seq_len_sharded = global_seq_len // sp_degree
                start_idx_reshard = seq_len_sharded * sp_rank
                end_idx_reshard = start_idx_reshard + seq_len_sharded if sp_rank < sp_degree - 1 else global_seq_len

                new_attention_mask = torch.narrow(
                    global_attention_mask, 1, start_idx_reshard, end_idx_reshard - start_idx_reshard
                )
                new_position_ids = torch.narrow(
                    global_position_ids, 1, start_idx_reshard, end_idx_reshard - start_idx_reshard
                )
                new_labels = torch.narrow(global_labels, 1, start_idx_reshard, end_idx_reshard - start_idx_reshard)
                new_inputs_embeds = torch.narrow(
                    global_inputs_embeds, 1, start_idx_reshard, end_idx_reshard - start_idx_reshard
                )

            return new_inputs_embeds, new_attention_mask, new_position_ids, new_labels

        device = inputs_embeds.device
        batch_size = inputs_embeds.shape[0]
        seqlens = [attention_mask[k].sum().item() for k in range(batch_size)]

        # Pack all sequences together
        inputs_embeds_p = [inputs_embeds[k][attention_mask[k]] for k in range(batch_size)]
        attention_mask_p = [torch.ones(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
        position_ids_p = [torch.arange(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
        labels_p = [labels[k][attention_mask[k]] for k in range(batch_size)]

        # Add one dummy token at the end of the packed sequence to ensure that `_get_unpacked_data` will be called
        inputs_embeds_p.append(torch.zeros(1, inputs_embeds.shape[-1], dtype=inputs_embeds.dtype, device=device))
        attention_mask_p.append(torch.tensor([0], dtype=torch.int, device=device))
        position_ids_p.append(torch.tensor([0], dtype=torch.int, device=device))
        labels_p.append(torch.tensor([IGNORE_INDEX], dtype=torch.int, device=device))

        # Mask the first token of each sequence to avoid contamination
        for label in labels_p:
            label[0] = IGNORE_INDEX

        # Batch the data
        inputs_embeds_p = torch.cat(inputs_embeds_p, dim=0).unsqueeze(0)
        attention_mask_p = torch.cat(attention_mask_p, dim=0).unsqueeze(0)
        position_ids_p = torch.cat(position_ids_p, dim=0).unsqueeze(0)
        labels_p = torch.cat(labels_p, dim=0).unsqueeze(0)

        if hasattr(
            self, "pad_to_multiple_of"
        ):  # related to quantization, please refer to ModelArguments for more information.
            assert len(labels_p.shape) == 2
            batch_size, max_length, cur_length = labels_p.shape[0], labels_p.shape[1], labels_p.shape[1]
            hidden_size = inputs_embeds_p.shape[-1]

            if max_length % self.pad_to_multiple_of != 0:
                max_length = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of
                difference = max_length - cur_length

                inputs_embeds_p = torch.cat(
                    (
                        inputs_embeds_p,
                        torch.full((batch_size, difference, hidden_size), self.llm.pad_token_id).to(inputs_embeds_p),
                    ),
                    dim=1,
                )
                labels_p = torch.cat((labels_p, torch.full((batch_size, difference), IGNORE_INDEX).to(labels_p)), dim=1)
                attention_mask_p = torch.cat(
                    (
                        attention_mask_p,
                        torch.zeros((batch_size, difference), dtype=torch.bool).to(attention_mask_p),
                    ),
                    dim=1,
                )
                position_ids_p = torch.cat(
                    (position_ids_p, torch.full((batch_size, difference), -1).to(position_ids_p)), dim=1
                )

        return inputs_embeds_p, attention_mask_p, position_ids_p, labels_p

    def get_xgr_logits_processor(self, response_format) -> List[LogitsProcessor]:
        raise NotImplementedError("This method is not implemented for VILA model.")
        # Convert response format to logits processor
        import xgrammar as xgr

        logging.info("[XGrammar] Compiling grammar for contrained output")

        if self.grammar_compiler is None:
            # logging.info(f"[XGrammar] {self.tokenizer}, {self.tokenizer.vocab_size}, {self.vocab_size}")
            self.grammar_compiler = xgr.GrammarCompiler(
                xgr.TokenizerInfo.from_huggingface(self.tokenizer, vocab_size=self.vocab_size)
            )

        if response_format.type == "json_schema":
            compiled_grammar = self.grammar_compiler.compile_json_schema(
                response_format.json_schema.schema_,
                indent=2,
            )
        else:
            compiled_grammar = self.grammar_compiler.compile_builtin_json_grammar()

        return [xgr.contrib.hf.LogitsProcessor(compiled_grammar)]

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        media: Optional[Dict[str, List[torch.Tensor]]] = None,
        images: Optional[torch.FloatTensor] = None,
        media_config: Optional[List] = 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,
        packing: bool = True,
        force_packing: bool = False,
        seqlens_in_batch: Optional[torch.LongTensor] = None,
        dpo_forward: bool = False,
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        self.freezed_module_patch()

        if images is not None:
            if media is not None:
                raise ValueError("Both 'media' and 'images' are provided. Please provide only one.")
            print("The 'images' argument is deprecated. Please use 'media' instead.")
            media = {"image": images}

        if media_config is None:
            media_config = defaultdict(dict)

        if inputs_embeds is None:
            inputs_embeds, labels, attention_mask = self._embed(input_ids, media, media_config, labels, attention_mask)

        if force_packing or (packing and self.training and not dpo_forward):
            if seqlens_in_batch is None:
                seqlens_in_batch = torch.sum(attention_mask, dim=1)
            set_seqlens_in_batch(seqlens_in_batch)

            (inputs_embeds, attention_mask, position_ids, labels) = self.repack_multimodal_data(
                inputs_embeds, attention_mask, position_ids, labels
            )

        outputs = self.llm(
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            labels=labels,
            **kwargs,
        )

        if self.training and getattr(self.config, "time_token_ids", []):
            outputs.loss = soft_cross_entropy(
                outputs.logits,
                labels,
                soft_tokens=self.config.time_token_ids,
                std=self.config.soft_ce_std,
            )

        if dpo_forward:
            return outputs.logits, labels

        return outputs
    @torch.inference_mode()
    def generate(
        self,
        input_ids: Optional[torch.FloatTensor] = None,
        media: Optional[Dict[str, List[torch.Tensor]]] = None,
        media_config: Dict[str, Dict[str, Any]] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        **generation_kwargs,
    ):
        inputs_embeds, _, attention_mask = self._embed(input_ids, media, media_config, None, attention_mask)
        return self.llm.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask, **generation_kwargs)

    @torch.inference_mode()
    def generate_content(
        self,
        prompt: Union[str, List],
        generation_config: Optional[GenerationConfig] = None,
        response_format = None,
    ) -> str:
        # TODO(zhijianl): Support directly taking conversation as input
        conversation = [{"from": "human", "value": prompt}]

        # Convert response format to logits processor
        if response_format:
            xgr_logits_processor = self.get_xgr_logits_processor(response_format)
        else:
            xgr_logits_processor = None

        # Extract media from the conversation

        # TODO (extract and preprocess should be done together, as the preprocess of image and video can be different, i.e. when dynamic res is used)
        media = extract_media(conversation, self.config)

        # Process media
        media_config = defaultdict(dict)
        for name in media:
            if name == "image":
                if len(media["image"]) == 1 and self.config.image_aspect_ratio in ["dynamic", "dynamic_s2"]:
                    self.config.image_processor = self.vision_tower.image_processor
                    if self.config.image_aspect_ratio == "dynamic":
                        images = process_image(media["image"][0], self.config, None, enable_dynamic_res=True).half()
                        conversation[0]["value"] = conversation[0]["value"].replace(
                            DEFAULT_IMAGE_TOKEN, f"{DEFAULT_IMAGE_TOKEN}\n" * images.shape[0]
                        )
                    else:
                        if type(self.config.s2_scales) is str:
                            self.config.s2_scales = list(map(int, self.config.s2_scales.split(",")))
                        images, block_sizes = process_image(
                            media["image"][0], self.config, None, enable_dynamic_s2=True
                        )
                        images = images.half()
                        media_config[name]["block_sizes"] = [block_sizes]
                else:
                    images = process_images(media["image"], self.vision_tower.image_processor, self.config).half()
                media[name] = [image for image in images]
            elif name == "video":
                if self.config.image_aspect_ratio == "dynamic" and self.config.video_max_tiles > 1:
                    media[name] = [
                        process_images(
                            images,
                            self.vision_tower.image_processor,
                            self.config,
                            enable_dynamic_res=True,
                            max_tiles=self.config.video_max_tiles,
                        ).half()
                        for images in media[name]
                    ]
                elif self.config.image_aspect_ratio == "dynamic_s2" and self.config.video_max_tiles > 1:
                    self.config.image_processor = self.vision_tower.image_processor
                    if type(self.config.s2_scales) is str:
                        self.config.s2_scales = list(map(int, self.config.s2_scales.split(",")))
                    media[name] = [
                        torch.cat(
                            [
                                process_image(
                                    image,
                                    self.config,
                                    None,
                                    enable_dynamic_s2=True,
                                    max_tiles=self.config.video_max_tiles,
                                )[0].half()
                                for image in images
                            ]
                        )
                        for images in media[name]
                    ]
                else:
                    media[name] = [
                        process_images(images, self.vision_tower.image_processor, self.config).half()
                        for images in media[name]
                    ]
            else:
                raise ValueError(f"Unsupported media type: {name}")

        # Tokenize the conversation
        input_ids = tokenize_conversation(conversation, self.tokenizer, add_generation_prompt=True).cuda().unsqueeze(0)

        # Set up the generation config
        generation_config = generation_config or self.default_generation_config

        # Generate the response
        try:
            output_ids = self.generate(
                input_ids=input_ids,
                media=media,
                media_config=media_config,
                generation_config=generation_config,
                logits_processor=xgr_logits_processor,  # structured generation
            )
        except ValueError:
            if not generation_config.do_sample:
                raise
            # FIXME(zhijianl): This is a temporary workaround for the sampling issue
            logging.warning("Generation failed with sampling, retrying with greedy decoding.")
            generation_config.do_sample = False
            output_ids = self.generate(
                input_ids=input_ids,
                media=media,
                media_config=media_config,
                generation_config=generation_config,
                logits_processor=xgr_logits_processor,
            )

        # Decode the response
        response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
        return response

    @property
    def default_generation_config(self) -> GenerationConfig:
        generation_config = copy.deepcopy(self.generation_config or GenerationConfig())
        if self.tokenizer.eos_token_id is None:
            raise ValueError("Tokenizer must have an EOS token")
        if generation_config.max_length == GenerationConfig().max_length:
            generation_config.max_length = self.tokenizer.model_max_length
        if generation_config.pad_token_id is None:
            generation_config.pad_token_id = self.tokenizer.pad_token_id or self.tokenizer.eos_token_id
        if generation_config.bos_token_id is None:
            generation_config.bos_token_id = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
        if generation_config.eos_token_id is None:
            generation_config.eos_token_id = self.tokenizer.eos_token_id
        return generation_config