| 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) |
|
|
| |
| 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) |
|
|
