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import os |
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import torch |
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from transformers import Trainer |
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from typing import Optional |
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def maybe_zero_3(param, ignore_status=False, name=None): |
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from deepspeed import zero |
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from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus |
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if hasattr(param, "ds_id"): |
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if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: |
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if not ignore_status: |
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print(name, 'no ignore status') |
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with zero.GatheredParameters([param]): |
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param = param.data.detach().cpu().clone() |
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else: |
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param = param.detach().cpu().clone() |
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return param |
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def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): |
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to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} |
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to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()} |
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return to_return |
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class ChatUniViTrainer(Trainer): |
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def _save_checkpoint(self, model, trial, metrics=None): |
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if 0 and getattr(self.args, 'tune_mm_mlp_adapter', False): |
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from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR |
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checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}" |
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run_dir = self._get_output_dir(trial=trial) |
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output_dir = os.path.join(run_dir, checkpoint_folder) |
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keys_to_match = ['mm_projector', "ctm", "block"] |
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if getattr(self.args, "use_im_start_end", False): |
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keys_to_match.extend(['embed_tokens', 'embed_in']) |
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weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match) |
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if self.args.local_rank == 0 or self.args.local_rank == -1: |
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self.model.config.save_pretrained(output_dir) |
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torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin')) |
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else: |
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super(ChatUniViTrainer, self)._save_checkpoint(model, trial, metrics) |
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def _save(self, output_dir: Optional[str] = None, state_dict=None): |
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if 0 and getattr(self.args, 'tune_mm_mlp_adapter', False): |
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pass |
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else: |
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super(ChatUniViTrainer, self)._save(output_dir, state_dict) |
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