noqueries_code/train.py

import os
import copy
from dataclasses import dataclass, field
import logging
import pathlib
from typing import Dict, Optional, Sequence
import torch
import glob
import transformers
import tokenizers
from blip3o.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_IDX, DEFAULT_IM_START_TOKEN_IDX
from torch.utils.data import Dataset
from blip3o.train.blip3o_trainer import blip3oTrainer
from blip3o import conversation as conversation_lib
from blip3o.model import *
from blip3o.mm_utils import tokenizer_image_token
from PIL import Image, ImageFile
from datasets import load_dataset, concatenate_datasets
from pathlib import Path
from datasets.utils.logging import set_verbosity_info
from transformers import logging as tf_logging
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoProcessor
import random
from blip3o.model.multimodal_encoder.eva_clip.eva_clip_processors import EvaClipImageTrainProcessor

ImageFile.LOAD_TRUNCATED_IMAGES = True
transform_und_images = T.Compose([T.Resize(448, interpolation=InterpolationMode.BICUBIC, antialias=True), T.CenterCrop(448)])

set_verbosity_info()
tf_logging.set_verbosity_info()

local_rank = None
from transformers import TrainerCallback

class GradCheckCallback(TrainerCallback):
    def on_step_end(self, args, state, control, **kwargs):
        model = kwargs["model"]
        for name, param in model.named_parameters():
            if "caption_embed" in name or "diffusion_connector" in name:
                if param.grad is None:
                    print(f"{name} has NO gradient!")
                else:
                    print(f"{name} grad mean: {param.grad.abs().mean().item():.6f}")

def rank0_print(*args):
    if local_rank == 0:
        print(*args)


from packaging import version


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
    version: Optional[str] = field(default="v0")
    freeze_backbone: bool = field(default=True)
    tune_mm_mlp_adapter: bool = field(default=False)
    vision_tower: Optional[str] = field(default=None)
    gen_vision_tower: Optional[str] = field(default=None)
    mm_vision_select_layer: Optional[int] = field(default=-1)  # default to the last layer
    pretrain_mm_mlp_adapter: Optional[str] = field(default=None)
    pretrain_gen_mlp_adapter: Optional[str] = field(default=None)
    vision_tower_pretrained: Optional[str] = field(default=None)
    mm_projector_type: Optional[str] = field(default="linear")
    gen_projector_type: Optional[str] = field(default="linear")
    mm_use_im_start_end: bool = field(default=False)
    mm_use_im_patch_token: bool = field(default=True)
    mm_patch_merge_type: Optional[str] = field(default="flat")
    mm_vision_select_feature: Optional[str] = field(default="patch")
    n_query: Optional[int] = field(default=729)  # clip 576, siglip 729
    n_und_query: Optional[int] = field(default=729)  # clip 576, siglip 729
    gen_pooling: Optional[str] = field(default="all")  # options are: pool2d_3, pool2d_9, seq_3, seq_9, seq_27
    diffusion_name_or_path: Optional[str] = field(default="Efficient-Large-Model/Sana_600M_1024px_diffusers")


@dataclass
class DataArguments:
    data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    lazy_preprocess: bool = False
    is_multimodal: bool = False
    image_folder: Optional[str] = field(default=None)
    journeyDB_folder: Optional[str] = field(default=None)
    shortcaption_image_folder: Optional[str] = field(default=None)
    data_type: Optional[str] = field(default="mix")
    image_aspect_ratio: str = "square"


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    remove_unused_columns: bool = field(default=False)
    freeze_mm_mlp_adapter: bool = field(default=False)
    mpt_attn_impl: Optional[str] = field(default="triton")
    model_max_length: int = field(
        default=512,
        metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
    )
    double_quant: bool = field(
        default=True,
        metadata={"help": "Compress the quantization statistics through double quantization."},
    )
    quant_type: str = field(
        default="nf4",
        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."},
    )
    bits: int = field(default=16, metadata={"help": "How many bits to use."})
    lora_enable: bool = False
    lora_r: int = 64
    lora_alpha: int = 16
    lora_dropout: float = 0.05
    lora_weight_path: str = ""
    lora_bias: str = "none"
    mm_projector_lr: Optional[float] = None
    group_by_modality_length: bool = field(default=False)
    ddp_find_unused_parameters: bool =True

ASPECT_RATIO_512 = {
    "0.25": [256.0, 1024.0],
    "0.26": [256.0, 992.0],
    "0.27": [256.0, 960.0],
    "0.28": [256.0, 928.0],
    "0.32": [288.0, 896.0],
    "0.33": [288.0, 864.0],
    "0.35": [288.0, 832.0],
    "0.4": [320.0, 800.0],
    "0.42": [320.0, 768.0],
    "0.48": [352.0, 736.0],
    "0.5": [352.0, 704.0],
    "0.52": [352.0, 672.0],
    "0.57": [384.0, 672.0],
    "0.6": [384.0, 640.0],
    "0.68": [416.0, 608.0],
    "0.72": [416.0, 576.0],
    "0.78": [448.0, 576.0],
    "0.82": [448.0, 544.0],
    "0.88": [480.0, 544.0],
    "0.94": [480.0, 512.0],
    "1.0": [1024.0, 1024.0],
    "1.07": [512.0, 480.0],
    "1.13": [544.0, 480.0],
    "1.21": [544.0, 448.0],
    "1.29": [576.0, 448.0],
    "1.38": [576.0, 416.0],
    "1.46": [608.0, 416.0],
    "1.67": [640.0, 384.0],
    "1.75": [672.0, 384.0],
    "2.0": [704.0, 352.0],
    "2.09": [736.0, 352.0],
    "2.4": [768.0, 320.0],
    "2.5": [800.0, 320.0],
    "2.89": [832.0, 288.0],
    "3.0": [864.0, 288.0],
    "3.11": [896.0, 288.0],
    "3.62": [928.0, 256.0],
    "3.75": [960.0, 256.0],
    "3.88": [992.0, 256.0],
    "4.0": [1024.0, 256.0],
}
print("Input size: ", ASPECT_RATIO_512["1.0"])

def maybe_zero_3(param, ignore_status=False, name=None):
    from deepspeed import zero
    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus

    if hasattr(param, "ds_id"):
        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
            if not ignore_status:
                logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
        with zero.GatheredParameters([param]):
            param = param.data.detach().cpu().clone()
    else:
        param = param.detach().cpu().clone()
    return param



def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
    return to_return




def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str, vision_tower: str):
    if trainer.deepspeed:
        torch.cuda.synchronize()
    keys_to_match = ["mm_projector"]
    if getattr(trainer.args, "use_im_start_end", False):
        keys_to_match.extend(["embed_tokens", "embed_in"])

    weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
    trainer.model.config.save_pretrained(output_dir)

    current_folder = output_dir.split("/")[-1]
    parent_folder = os.path.dirname(output_dir)
    if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
        if current_folder.startswith("checkpoint-"):
            mm_projector_folder = os.path.join(parent_folder, "mm_projector")
            os.makedirs(mm_projector_folder, exist_ok=True)
            torch.save(
                weight_to_save,
                os.path.join(mm_projector_folder, f"{current_folder}.bin"),
            )
        else:
            torch.save(weight_to_save, os.path.join(output_dir, f"mm_projector.bin"))

    keys_to_match = ["gen_projector"]
    if getattr(trainer.args, "use_im_start_end", False):
        keys_to_match.extend(["embed_tokens", "embed_in"])

    weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
    trainer.model.config.save_pretrained(output_dir)

    current_folder = output_dir.split("/")[-1]
    parent_folder = os.path.dirname(output_dir)
    if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
        if current_folder.startswith("checkpoint-"):
            mm_projector_folder = os.path.join(parent_folder, "gen_projector")
            os.makedirs(mm_projector_folder, exist_ok=True)
            torch.save(
                weight_to_save,
                os.path.join(mm_projector_folder, f"{current_folder}.bin"),
            )
        else:
            torch.save(weight_to_save, os.path.join(output_dir, f"gen_projector.bin"))

    if trainer.deepspeed:
        torch.cuda.synchronize()
        trainer.save_model(output_dir)
        return

    state_dict = trainer.model.state_dict()
    if trainer.args.should_save:
        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
        del state_dict
        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa


def smart_tokenizer_and_embedding_resize(
    special_tokens_dict: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
):


    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
    model.resize_token_embeddings(len(tokenizer))

    if num_new_tokens > 0:
        input_embeddings = model.get_input_embeddings().weight.data
        input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
        input_embeddings[-num_new_tokens:] = input_embeddings_avg




def preprocess_multimodal(sources: Sequence[str], data_args: DataArguments) -> Dict:
    is_multimodal = data_args.is_multimodal
    if not is_multimodal: return sources
    und_placeholder = "<|vision_start|>" + "<|image_pad|>" * data_args.n_und_query + "<|vision_end|>"
    gen_placeholder = ""
    # "[IMG]" + "<image>" * data_args.n_query + "[/IMG]"
    inst_type = None
    for source in sources:  # [instance]
        for sentence in source:
            if sentence["from"] == "human" and "<image>" in sentence["value"]:
                sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, und_placeholder).strip()
                inst_type = "und"
            elif sentence["from"] == "gpt" and "<image>" in sentence["value"]:
                sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, gen_placeholder).strip()
                inst_type = "gen"
    return sources, inst_type




def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
    roles = {"human": "user", "gpt": "assistant"}

    tokenizer = copy.deepcopy(tokenizer)
    chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
    tokenizer.chat_template = chat_template

    # Apply prompt templates
    input_ids, targets = [], []
    for i, source in enumerate(sources):
        if roles[source[0]["from"]] != roles["human"]:
            source = source[1:]

        input_id, target = [], []

        # New version, use apply chat template
        # Build system message for each sentence
        input_id += tokenizer.apply_chat_template([{"role" : "system", "content" : system_message}])
        target += [IGNORE_INDEX] * len(input_id)

        for conv in source:
            try:
                role = conv["role"]
                content = conv["content"]
            except:
                role = conv["from"]
                content = conv["value"]

            role =  roles.get(role, role)
            
            conv = [{"role" : role, "content" : content}]
            encode_id = tokenizer.apply_chat_template(conv)
            input_id += encode_id
            if role in ["user", "system"]:
                target += [IGNORE_INDEX] * len(encode_id)
            else:
                target += encode_id
        

                    
        assert len(input_id) == len(target), f"{len(input_id)} != {len(target)}"

        input_ids.append(input_id)
        targets.append(target)
    input_ids = torch.tensor(input_ids, dtype=torch.long)
    targets = torch.tensor(targets, dtype=torch.long)

    return dict(
        input_ids=input_ids,  # tensor(bs x seq_len)
        labels=targets,  # tensor(bs x seq_len)
    )

def get_closest_ratio(height: float, width: float, ratios: dict):
    aspect_ratio = height / width
    closest_ratio = "1.0" #min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
    return ratios[closest_ratio], float(closest_ratio)



class LazySupervisedMixDataset(Dataset):
    def __init__(
        self,
        data_path: str,
        tokenizer: transformers.PreTrainedTokenizer,
        data_args: DataArguments,
    ):
        super(LazySupervisedMixDataset, self).__init__()

        self.data_args = data_args
        list_data_dict = []

        ###################################### text to image ####################################### 
        data_files = glob.glob(os.path.join(self.data_args.image_folder, "*.tar"))
        #data_files = glob.glob(os.path.join('/proj/cvl/users/x_fahkh2/BLIP3o/dataset/BLIP3o-Pretrain-Long-Caption', "*.tar")) + glob.glob(os.path.join('/proj/cvl/users/x_fahkh2/BLIP3o/dataset/BLIP3o-Pretrain-Short-Caption', "*.tar")) + glob.glob(os.path.join('/proj/cvl/users/x_fahkh2/BLIP3o/dataset/BLIP3o-Pretrain-JourneyDB', "*.tar"))
        train_dataset = load_dataset("webdataset", data_files=data_files, split="train", num_proc=32)
        train_dataset = train_dataset.rename_column("jpg", "image")
        train_dataset = train_dataset.add_column('type', len(train_dataset) * ['T2I'])
        train_dataset = train_dataset.add_column('image_path', len(train_dataset) * [None])
        train_dataset = train_dataset.remove_columns([col for col in train_dataset.column_names if not col in (
            ["image", "txt", "type", "image_path"])])
        print(f"finish loading image {len(train_dataset)}")
        list_data_dict.append(train_dataset)
            

        if len(list_data_dict) > 1:
            list_data_dict = concatenate_datasets(list_data_dict)
        else:
            list_data_dict = list_data_dict[0]
        list_data_dict = list_data_dict.shuffle(seed=42)

        rank0_print(f"Total number of training instance: {len(list_data_dict)}")
        self.tokenizer = tokenizer
        self.list_data_dict = list_data_dict

    def __len__(self):
        return len(self.list_data_dict)

    @property
    def lengths(self):
        length_list = []
        for sample in self.list_data_dict:
            img_tokens = 128 if "image" in sample else 0
            length_list.append(sum(len(conv["value"].split()) for conv in sample["conversations"]) + img_tokens)
        return length_list

    @property
    def modality_lengths(self):
        length_list = []
        for sample in self.list_data_dict:
            cur_len = sum(len(conv["value"].split()) for conv in sample["conversations"])
            cur_len = cur_len if "image" in sample else -cur_len
            length_list.append(cur_len)
        return length_list
    
    def _safe_img_process(self, imgs):
        try:
            out = []
            for img in imgs:
                ori_h, ori_w = img.height, img.width
                closest_size, closest_ratio = get_closest_ratio(ori_h, ori_w, ASPECT_RATIO_512)
                closest_size = [int(x) for x in closest_size]
                if closest_size[0] / ori_h > closest_size[1] / ori_w:
                    resize_size = closest_size[0], int(ori_w * closest_size[0] / ori_h)
                else:
                    resize_size = int(ori_h * closest_size[1] / ori_w), closest_size[1]
                transform = T.Compose([
                    T.Lambda(lambda img: img.convert("RGB")),
                    T.Resize(resize_size, interpolation=InterpolationMode.BICUBIC),  # Image.BICUBIC
                    T.CenterCrop(closest_size),
                    T.ToTensor(),
                    T.Normalize([0.5], [0.5]),
                    ])
                out.append(transform(img))
            return out
        except Exception as e:
            print(f"Corrupted image during processing: {e}")
            return None

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:

        while True:
            try:
                sources = self.list_data_dict[i]
                sources["conversations"] = [
                    {"from": "human", "value": f"Please generate image based on the following caption: {sources['txt']}"},
                    {"from": "gpt", "value": "<image>"},
                ]
                image_files = self.list_data_dict[i]["image"]
                if not isinstance(image_files, list):
                    image_files = [image_files]

                is_corrupt = False
                images = []
                for img in image_files:
                    img = img.convert("RGB")
                    images.append(img)      
        
                processed_images = self._safe_img_process(images)
                if processed_images is None:
                    print("Corrupted image during transform, picking new sample.")
                    i = random.randint(0, len(self.list_data_dict) - 1)
                    continue 
                # just replace <image> with "" in generation tasks
                sources, inst_type = preprocess_multimodal(copy.deepcopy([sources["conversations"]]), self.data_args)
                data_dict = preprocess_qwen(sources, self.tokenizer, has_image=("image" in self.list_data_dict[i]))
                if isinstance(i, int):
                    data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])

                data_dict["gen_image"] = processed_images[0]
                data_dict["ids"] = self.list_data_dict[i]["id"] if "id" in self.list_data_dict[i] else "unk"
                return data_dict
            except Exception as e:
                print(f"[WARN] Skipping corrupted sample {i}: {e}")
                i = random.randint(0, len(self.list_data_dict) - 1)
                continue

@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        input_ids, labels, ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "ids"))
        multi_input_ids = []
        multi_labels = []
        i_s_pos = []
        for input_id, label in zip(input_ids, labels):
            input_id = input_id[: self.tokenizer.model_max_length - 17]
            label = label[: self.tokenizer.model_max_length - 17]
            i_s_pos.append(input_id.shape[0]+1)
            img_id = torch.full((17,), IMAGE_TOKEN_IDX, dtype=input_id.dtype, device=input_id.device)
            img_id[0] = DEFAULT_IM_START_TOKEN_IDX
            # input_id = torch.cat([input_id, img_id])
            img_label = torch.full((17,), IMAGE_TOKEN_IDX, dtype=label.dtype, device=label.device)
            img_label[0] = DEFAULT_IM_START_TOKEN_IDX
            # label = torch.cat([label, img_label])
            multi_input_ids.append(input_id)
            multi_labels.append(label)

        input_ids = multi_input_ids
        labels = multi_labels

        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
        if input_ids.shape[1] > self.tokenizer.model_max_length:
            print(f"Warning input with length {input_ids.shape[1]} is longer than max length {self.tokenizer.model_max_length}")
        input_ids = input_ids[:, : self.tokenizer.model_max_length]
        labels = labels[:, : self.tokenizer.model_max_length]
        attention_mask = input_ids.ne(self.tokenizer.pad_token_id)
        batch = dict(
            input_ids=input_ids,
            labels=labels,
            attention_mask=attention_mask,
        )

        batch_gen_images = []
        batch_und_images = []
        batch_grid_thw = []

        for instance in instances:
            if "gen_image" in instance:
                batch_gen_images.append(instance["gen_image"])

        if len(batch_gen_images) > 0:
            if all(x is not None and y.shape == batch_gen_images[0][0].shape for x in batch_gen_images for y in x):
                batch["gen_image"] = torch.cat([images.unsqueeze(0) for images in batch_gen_images], dim=0)
            else:
                batch["gen_image"] = batch_gen_images
        else:
            batch["gen_image"] = None


        for instance in instances:
            if "und_image" in instance:
                batch_und_images.append(instance["und_image"].unsqueeze(0))  ## 1*1024*1176
                batch_grid_thw.append(instance["grid_thw"])  ## 1*3


        # print(f"batch_und_images {batch_und_images}")
        if len(batch_und_images) > 0:
            batch["und_image"] = torch.cat([images for images in batch_und_images], dim=0)
            batch["grid_thw"] = torch.cat([images for images in batch_grid_thw], dim=0)
        else:
            batch["und_image"] = None
            batch["grid_thw"] = None

        batch["ids"] = ids
        batch["i_s_pos"] = i_s_pos
        return batch


def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict:
    train_dataset = LazySupervisedMixDataset(tokenizer=tokenizer, data_path=data_args.data_path, data_args=data_args)
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator)

def train(attn_implementation=None):
    global local_rank

    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    print(model_args, data_args, training_args)
    local_rank = training_args.local_rank
    compute_dtype = torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32)

    bnb_model_from_pretrained_args = {}
    if training_args.bits in [4, 8]:
        from transformers import BitsAndBytesConfig

        bnb_model_from_pretrained_args.update(
            dict(
                device_map={"": training_args.device},
                load_in_4bit=training_args.bits == 4,
                load_in_8bit=training_args.bits == 8,
                quantization_config=BitsAndBytesConfig(
                    load_in_4bit=training_args.bits == 4,
                    load_in_8bit=training_args.bits == 8,
                    llm_int8_skip_modules=["mm_projector"],
                    llm_int8_threshold=6.0,
                    llm_int8_has_fp16_weight=False,
                    bnb_4bit_compute_dtype=compute_dtype,
                    bnb_4bit_use_double_quant=training_args.double_quant,
                    bnb_4bit_quant_type=training_args.quant_type,  # {'fp4', 'nf4'}
                ),
            )
        )
        
    model = blip3oFastForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        # attn_implementation=attn_implementation,
        torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
        **bnb_model_from_pretrained_args,
    )
   
    
    model.config.use_cache = False
    
    if model_args.freeze_backbone:
        for (n, p) in model.get_model().named_parameters():
            p.requires_grad = False
        for (n, p) in model.get_vision_tower().named_parameters(): 
            p.requires_grad = False
        for (n, p) in model.lm_head.named_parameters():
            p.requires_grad = False
    
    #for (n, p) in model.get_model().named_parameters():
    #    p.requires_grad = True
    #for (n, p) in model.get_vision_tower().named_parameters():
    #    p.requires_grad = False

    #for (n, p) in model.get_model().embed_tokens.named_parameters():
    #    p.requires_grad=True
    
    if training_args.gradient_checkpointing:
        if hasattr(model, "enable_input_require_grads"):
            model.enable_input_require_grads()
        else:

            def make_inputs_require_grad(module, input, output):
                output.requires_grad_(True)

            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
    
    try:
        tokenizer = AutoProcessor.from_pretrained(model_args.model_name_or_path).tokenizer
    except Exception as e:
        tokenizer = AutoProcessor.from_pretrained(model_args.model_name_or_path)
        
    tokenizer.model_max_length = training_args.model_max_length

    # tokenizer.pad_token = tokenizer.unk_token
    if tokenizer.pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(
                pad_token="<pad>",
                additional_special_tokens=["[IMG]", "[/IMG]", "<image>"],
            ),
            tokenizer=tokenizer,
            model=model,
        )
    elif not "<image>" in tokenizer.get_added_vocab():
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(additional_special_tokens=["[IMG]", "[/IMG]", "<image>"]),
            tokenizer=tokenizer,
            model=model,
        )
    if model_args.version in conversation_lib.conv_templates:
        conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
    else:
        conversation_lib.default_conversation = conversation_lib.conv_templates["llama3"]
    rank0_print(f"Using conversation format: {conversation_lib.default_conversation.version}")



    # if model_args.vision_tower is not None:
    model.get_model().initialize_vision_modules(model_args=model_args, fsdp=training_args.fsdp)
    image_processor = model.get_model().get_vision_tower().image_processor
    data_args.gen_image_processor = image_processor
    data_args.image_processor = image_processor

    data_args.is_multimodal = True
    data_args.n_query = model_args.n_query
    data_args.n_und_query = model_args.n_und_query

    model.config.image_aspect_ratio = data_args.image_aspect_ratio
    model.config.tokenizer_padding_side = tokenizer.padding_side
    model.config.tokenizer_model_max_length = tokenizer.model_max_length

    model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter

    model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter

    # Calculate total parameters and trainable parameters
    total_params = sum(p.numel() for p in model.get_model().parameters())
    trainable_params = sum(p.numel() for p in model.get_model().parameters() if p.requires_grad)

    print(f"Total parameters: {total_params}")
    print(f"Trainable parameters: {trainable_params}")


    model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end
    model.config.mm_projector_lr = training_args.mm_projector_lr
    training_args.use_im_start_end = model_args.mm_use_im_start_end
    model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token
    # TODO: what is this?
    model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer)
    model.config.pad_token_id = tokenizer.pad_token_id

    data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)

    trainer = blip3oTrainer(
        model=model,
        tokenizer=tokenizer,
        args=training_args,
	#callbacks=[GradCheckCallback],
        **data_module,
    )
    from tabulate import tabulate

    if trainer.is_world_process_zero():
        stat = []
        for i, (n, p) in enumerate(trainer.model.named_parameters()):
            stat.append([i, n, p.shape, p.requires_grad])
        print(tabulate(stat, headers=["idx", "name", "shape", "trainable"]))
    
    '''
    from safetensors.torch import load_file
    import json
    import pathlib

    # ---- Load model.safetensors if it exists ----
    checkpoint_dir = pathlib.Path(training_args.output_dir)
    safetensor_path = checkpoint_dir / "model.safetensors"
    trainer_state_path = checkpoint_dir / "trainer_state.json"
    
    if safetensor_path.exists():
        print(f"Loading weights from {safetensor_path}")
        state_dict = load_file(safetensor_path)
        new_state_dict = {}
        for k, v in state_dict.items():
            new_key = k.replace("model.", "", 1) if k.startswith("model.") else k
            new_state_dict[new_key] = v

        # print all keys
        #print("🔑 Keys in checkpoint:")
        #for k in state_dict.keys():
        #    print(k, state_dict[k].shape)

        missing, unexpected = model.get_model().load_state_dict(new_state_dict, strict=False)
        print("✅ Loaded parameters:")
        for k in new_state_dict.keys():
            if k not in missing:
                print(f"  {k} {tuple(new_state_dict[k].shape)}")


    # Restore last global step
    if trainer_state_path.exists():
        with open(trainer_state_path, "r") as f:
            trainer_state = json.load(f)
        last_global_step = trainer_state.get("global_step", 0)
        last_lr = trainer_state.get("learning_rate", trainer.args.learning_rate)
        trainer.state.global_step = last_global_step
        # Reset optimizer with last learning rate
        trainer.create_optimizer_and_scheduler(num_training_steps=trainer.args.max_steps)
        optimizer = trainer.optimizer
        #lr_scheduler = trainer.lr_scheduler

        for param_group in optimizer.param_groups:
            param_group['lr'] = last_lr
        trainer.optimizer = optimizer
        print(f"✅ Restored global step: {last_global_step}, learning rate: {last_lr}")


    '''
    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
        trainer.train(resume_from_checkpoint=True)
    else:
        trainer.train()
    trainer.save_state()

    model.config.use_cache = True
    safe_save_model_for_hf_trainer(
        trainer=trainer,
        output_dir=training_args.output_dir,
        vision_tower=model_args.vision_tower,
    )


if __name__ == "__main__":
    train()