src/transformers/trainer.py/Trainer/_wrap_model
class Trainer:    def _wrap_model(self, model, training=True):
        if is_sagemaker_mp_enabled():
            # Wrapping the base model twice in a DistributedModel will raise an error.
            if isinstance(self.model_wrapped, smp.model.DistributedModel):
                return self.model_wrapped
            return smp.DistributedModel(model, backward_passes_per_step=self.args.gradient_accumulation_steps)

        # already initialized its own DDP and AMP
        if self.deepspeed:
            return self.deepspeed

        # train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
        if unwrap_model(model) is not model:
            return model

        # Mixed precision training with apex (torch < 1.6)
        if self.use_apex and training:
            model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)

        # Multi-gpu training (should be after apex fp16 initialization)
        if self.args.n_gpu > 1:
            model = nn.DataParallel(model)

        # Note: in torch.distributed mode, there's no point in wrapping the model
        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.
        if not training:
            return model

        # Distributed training (should be after apex fp16 initialization)
        if self.sharded_ddp is not None:
            # Sharded DDP!
            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
                model = ShardedDDP(model, self.optimizer)
            else:
                mixed_precision = self.args.fp16
                cpu_offload = ShardedDDPOption.OFFLOAD in self.args.sharded_ddp
                zero_3 = self.sharded_ddp == ShardedDDPOption.ZERO_DP_3
                # XXX: Breaking the self.model convention but I see no way around it for now.
                if ShardedDDPOption.AUTO_WRAP in self.args.sharded_ddp:
                    model = auto_wrap(model)
                self.model = model = FullyShardedDDP(
                    model,
                    mixed_precision=mixed_precision,
                    reshard_after_forward=zero_3,
                    cpu_offload=cpu_offload,
                ).to(self.args.device)

        elif is_sagemaker_dp_enabled():
            model = DDP(model, device_ids=[dist.get_local_rank()], broadcast_buffers=False)
        elif self.args.local_rank != -1:
            if self.args.ddp_find_unused_parameters is not None:
                find_unused_parameters = self.args.ddp_find_unused_parameters
            elif isinstance(model, PreTrainedModel):
                # find_unused_parameters breaks checkpointing as per
                # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
                find_unused_parameters = not getattr(model.config, "gradient_checkpointing", False)
            else:
                find_unused_parameters = True
            model = nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank],
                output_device=self.args.local_rank,
                find_unused_parameters=find_unused_parameters,
            )

        return model