safe_save_trainer.py

import os
from pathlib import Path
from packaging import version
from transformers import is_torch_tpu_available
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.utils import is_sagemaker_mp_enabled, WEIGHTS_NAME, logging
from transformers.trainer_utils import ShardedDDPOption
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
from typing import Optional
from trl import SFTTrainer

if is_sagemaker_mp_enabled():
    import smdistributed.modelparallel.torch as smp
    from smdistributed.modelparallel import __version__ as SMP_VERSION

    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")

    from transformers.trainer_pt_utils import smp_forward_backward, smp_forward_only, smp_gather, smp_nested_concat
else:
    IS_SAGEMAKER_MP_POST_1_10 = False

logger = logging.get_logger(__name__)

class SFT_SafeSaveTrainer(SFTTrainer):
    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
        """
        Will save the model, so you can reload it using `from_pretrained()`.
        Will only save from the main process.
        """

        if output_dir is None:
            output_dir = self.args.output_dir

        if is_torch_tpu_available():
            self._save_tpu(output_dir)
        elif is_sagemaker_mp_enabled():
            # Calling the state_dict needs to be done on the wrapped model and on all processes.
            os.makedirs(output_dir, exist_ok=True)
            state_dict = self.model_wrapped.state_dict()
            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
            if IS_SAGEMAKER_MP_POST_1_10:
                # 'user_content.pt' indicates model state_dict saved with smp >= 1.10
                Path(os.path.join(output_dir, "user_content.pt")).touch()
        elif (
            ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp
            or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
            or self.fsdp is not None
        ):
            full_state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
            with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, full_state_dict_config):
                state_dict = self.model.state_dict()

            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
        elif self.deepspeed:
            # this takes care of everything as long as we aren't under zero3
            if self.args.should_save:
                self._save(output_dir)

            if is_deepspeed_zero3_enabled():
                # It's too complicated to try to override different places where the weights dump gets
                # saved, so since under zero3 the file is bogus, simply delete it. The user should
                # either user deepspeed checkpoint to resume or to recover full weights use
                # zero_to_fp32.py stored in the checkpoint.
                if self.args.should_save:
                    file = os.path.join(output_dir, WEIGHTS_NAME)
                    if os.path.isfile(file):
                        # logger.info(f"deepspeed zero3: removing {file}, see zero_to_fp32.py to recover weights")
                        os.remove(file)

                # now save the real model if stage3_gather_16bit_weights_on_model_save=True
                # if false it will not be saved.
                # This must be called on all ranks
                if not self.deepspeed.save_16bit_model(output_dir, WEIGHTS_NAME):
                    logger.warning(
                        "deepspeed.save_16bit_model didn't save the model, since"
                        " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use"
                        " zero_to_fp32.py to recover weights"
                    )
                    self.deepspeed.save_checkpoint(output_dir)

        elif self.args.should_save:
            self._save(output_dir)

        # Push to the Hub when `save_model` is called by the user.
        if self.args.push_to_hub and not _internal_call:
            self.push_to_hub(commit_message="Model save")