VisualSearch/model/llava/train/llava_trainer.py

import os
from typing import Optional

import torch
from transformers import Trainer


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:
                print(name, "no ignore 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, name=k).cpu()
        for k, v in to_return.items()
    }
    return to_return


class LLaVATrainer(Trainer):
    def _save_checkpoint(self, model, trial, metrics=None):
        if getattr(self.args, "tune_mm_mlp_adapter", False):
            from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR

            checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"

            run_dir = self._get_output_dir(trial=trial)
            output_dir = os.path.join(run_dir, checkpoint_folder)

            # Only save Adapter
            keys_to_match = ["mm_projector"]
            if getattr(self.args, "use_im_start_end", False):
                keys_to_match.extend(["embed_tokens", "embed_in"])

            weight_to_save = get_mm_adapter_state_maybe_zero_3(
                self.model.named_parameters(), keys_to_match
            )

            if self.args.local_rank == 0 or self.args.local_rank == -1:
                self.model.config.save_pretrained(output_dir)
                torch.save(
                    weight_to_save, os.path.join(output_dir, f"mm_projector.bin")
                )
        else:
            super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)

    def _save(self, output_dir: Optional[str] = None, state_dict=None):
        if getattr(self.args, "tune_mm_mlp_adapter", False):
            pass
        else:
            super(LLaVATrainer, self)._save(output_dir, state_dict)