pointllm/train/pointllm_trainer.py

import os
import torch
import torch.nn as nn

from transformers import Trainer
from typing import Optional


def unwrap_model(model: nn.Module) -> nn.Module:
    """
    Recursively unwraps a model from potential containers (as used in distributed training).

    Args:
        model (`torch.nn.Module`): The model to unwrap.
    """
    # since there could be multiple levels of wrapping, unwrap recursively
    if hasattr(model, "module"):
        return unwrap_model(model.module)
    else:
        return model


class PointLLMTrainer(Trainer):

    def _save(self, output_dir: Optional[str] = None, state_dict=None):
        print("no save!!!!!!1")
        pass
        if getattr(self.args, 'tune_mm_mlp_adapter', False):
            # Save the model
            _state_dict = state_dict
            if _state_dict is None:
                # Only save the model itself if we are using distributed training
                model_to_save = unwrap_model(self.model)
                _state_dict = model_to_save.state_dict()

            weight_to_save = {}
            keys_to_match = ['point_proj', 'embed_tokens', 'embed_in']
            for k, v in _state_dict.items():
                if any(key_match in k for key_match in keys_to_match):
                    weight_to_save[k] = v

            current_folder = output_dir.split('/')[-1]
            parent_folder = os.path.dirname(output_dir)
            if current_folder.startswith('checkpoint-'):
                mm_projector_folder = os.path.join(parent_folder, "point_proj")
                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'point_proj.bin'))

        super(PointLLMTrainer, self)._save(output_dir, state_dict)