def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", Dict[str, Any]] = None):
        """
        Main training entry point.

        Args:
            model_path (:obj:`str`, `optional`):
                Local path to the model if the model to train has been instantiated from a local path. If present,
                training will resume from the optimizer/scheduler states loaded here.
            trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`):
                The trial run or the hyperparameter dictionary for hyperparameter search.
        
        The main difference between ours and Huggingface's original implementation is that we 
        also load model_args when reloading best checkpoints for evaluation.
        """
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)

        # Model re-init
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            set_seed(self.args.seed)

            model = self.call_model_init(trial)
            if not self.is_model_parallel:
                model = model.to(self.args.device)

            self.model = model
            self.model_wrapped = model

            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Keeping track whether we can can len() on the dataset or not
        train_dataset_is_sized = isinstance(self.train_dataset, collections.abc.Sized)
        
        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()

        # Setting up training control variables:
        # number of training epochs: num_train_epochs
        # number of training steps per epoch: num_update_steps_per_epoch
        # total number of training steps to execute: max_steps
        if train_dataset_is_sized:
            num_update_steps_per_epoch = len(train_dataloader) // self.args.gradient_accumulation_steps
            num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
            if self.args.max_steps > 0:
                max_steps = self.args.max_steps
                num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int(
                    self.args.max_steps % num_update_steps_per_epoch > 0
                )
            else:
                max_steps = math.ceil(self.args.num_train_epochs * num_update_steps_per_epoch)
                num_train_epochs = math.ceil(self.args.num_train_epochs)
        else:
            # see __init__. max_steps is set when the dataset has no __len__
            max_steps = self.args.max_steps
            num_train_epochs = 1
            num_update_steps_per_epoch = max_steps

        if self.args.deepspeed:
            model, optimizer, lr_scheduler = init_deepspeed(self, num_training_steps=max_steps)
            self.model = model.module
            self.model_wrapped = model  # will get further wrapped in DDP
            self.deepspeed = model  # DeepSpeedEngine object
            self.optimizer = optimizer
            self.lr_scheduler = lr_scheduler
        else:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState()
        self.state.is_hyper_param_search = trial is not None

        # Check if saved optimizer or scheduler states exist
        self._load_optimizer_and_scheduler(model_path)

        model = self.model_wrapped

        # Mixed precision training with apex (torch < 1.6)
        if self.use_apex:
            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 = torch.nn.DataParallel(model)

        # Distributed training (should be after apex fp16 initialization)
        if self.sharded_dpp:
            model = ShardedDDP(model, self.optimizer)
        elif self.args.local_rank != -1:
            model = torch.nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank],
                output_device=self.args.local_rank,
                find_unused_parameters=(
                    not getattr(model.config, "gradient_checkpointing", False)
                    if isinstance(model, PreTrainedModel)
                    else True
                ),
            )
            # find_unused_parameters breaks checkpointing as per
            # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021

        # for the rest of this function `model` is the outside model, whether it was wrapped or not
        if model is not self.model:
            self.model_wrapped = model

        # important: at this point:
        # self.model         is the Transformers Model
        # self.model_wrapped is DDP(Transformers Model), DDP(Deepspeed(Transformers Model)), etc.

        # Train!
        if is_torch_tpu_available():
            total_train_batch_size = self.args.train_batch_size * xm.xrt_world_size()
        else:
            total_train_batch_size = (
                self.args.train_batch_size
                * self.args.gradient_accumulation_steps
                * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1)
            )

        num_examples = (
            self.num_examples(train_dataloader)
            if train_dataset_is_sized
            else total_train_batch_size * self.args.max_steps
        )

        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Num Epochs = {num_train_epochs}")
        logger.info(f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size}")
        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
        logger.info(f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
        logger.info(f"  Total optimization steps = {max_steps}")

        self.state.epoch = 0
        start_time = time.time()
        epochs_trained = 0
        steps_trained_in_current_epoch = 0

        # Check if continuing training from a checkpoint
        if model_path and os.path.isfile(os.path.join(model_path, "trainer_state.json")):
            self.state = TrainerState.load_from_json(os.path.join(model_path, "trainer_state.json"))
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            if not self.args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
                steps_trained_in_current_epoch *= self.args.gradient_accumulation_steps
            else:
                steps_trained_in_current_epoch = 0

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info(f"  Continuing training from epoch {epochs_trained}")
            logger.info(f"  Continuing training from global step {self.state.global_step}")
            if not self.args.ignore_data_skip:
                logger.info(
                    f"  Will skip the first {epochs_trained} epochs then the first {steps_trained_in_current_epoch} "
                    "batches in the first epoch."
                )

        # Update the references
        self.callback_handler.model = self.model
        self.callback_handler.optimizer = self.optimizer
        self.callback_handler.lr_scheduler = self.lr_scheduler
        self.callback_handler.train_dataloader = train_dataloader
        self.state.trial_name = self.hp_name(trial) if self.hp_name is not None else None
        self.state.trial_params = hp_params(trial) if trial is not None else None
        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()

        # tr_loss is a tensor to avoid synchronization of TPUs through .item()
        tr_loss = torch.tensor(0.0).to(self.args.device)
        # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
        self._total_loss_scalar = 0.0
        self._globalstep_last_logged = 0
        self._total_flos = self.state.total_flos
        model.zero_grad()

        self.control = self.callback_handler.on_train_begin(self.args, self.state, self.control)

        # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
        if not self.args.ignore_data_skip:
            for epoch in range(epochs_trained):
                # We just need to begin an iteration to create the randomization of the sampler.
                for _ in train_dataloader:
                    break
        for epoch in range(epochs_trained, num_train_epochs):
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)
            epoch_iterator = train_dataloader

            # Reset the past mems state at the beginning of each epoch if necessary.
            if self.args.past_index >= 0:
                self._past = None

            steps_in_epoch = len(train_dataloader) if train_dataset_is_sized else self.args.max_steps
            self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)

            assert train_dataset_is_sized, "currently we only support sized dataloader!"

            inputs = None
            last_inputs = None
            for step, inputs in enumerate(epoch_iterator):
                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    continue

                if (step + 1) % self.args.gradient_accumulation_steps == 0:
                    self.control = self.callback_handler.on_step_begin(self.args, self.state, self.control)

                if ((step + 1) % self.args.gradient_accumulation_steps != 0) and self.args.local_rank != -1:
                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
                    with model.no_sync():
                        tr_loss += self.training_step(model, inputs)
                else:
                    tr_loss += self.training_step(model, inputs)
                self._total_flos += self.floating_point_ops(inputs)

                if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    steps_in_epoch <= self.args.gradient_accumulation_steps
                    and (step + 1) == steps_in_epoch
                ):
                    # Gradient clipping
                    if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0 and not self.deepspeed:
                        # deepspeed does its own clipping

                        if self.use_amp:
                            # AMP: gradients need unscaling
                            self.scaler.unscale_(self.optimizer)

                        if hasattr(self.optimizer, "clip_grad_norm"):
                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
                            self.optimizer.clip_grad_norm(self.args.max_grad_norm)
                        else:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            torch.nn.utils.clip_grad_norm_(
                                amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
                                self.args.max_grad_norm,
                            )

                    # Optimizer step
                    if is_torch_tpu_available():
                        xm.optimizer_step(self.optimizer)
                    elif self.use_amp:
                        self.scaler.step(self.optimizer)
                        self.scaler.update()
                    else:
                        self.optimizer.step()
                    
                    self.lr_scheduler.step()

                    model.zero_grad()

                    self.state.global_step += 1
                    self.state.epoch = epoch + (step + 1) / steps_in_epoch
                    self.control = self.callback_handler.on_step_end(self.args, self.state, self.control)

                    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)

                if self.control.should_epoch_stop or self.control.should_training_stop:
                    break

            self.control = self.callback_handler.on_epoch_end(self.args, self.state, self.control)
            self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)

            if self.args.tpu_metrics_debug or self.args.debug:
                if is_torch_tpu_available():
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report())
                else:
                    logger.warning(
                        "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                        "configured. Check your training configuration if this is unexpected."
                    )
            if self.control.should_training_stop:
                break

        if self.args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of training
            delattr(self, "_past")

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        if self.args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            logger.info(
                f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
            )
            if isinstance(self.model, PreTrainedModel):
                self.model = self.model.from_pretrained(self.state.best_model_checkpoint, model_args=self.model_args)
                if not self.is_model_parallel:
                    self.model = self.model.to(self.args.device)
            else:
                state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME))
                self.model.load_state_dict(state_dict)

            if self.deepspeed:
                self.deepspeed.load_checkpoint(
                    self.state.best_model_checkpoint, load_optimizer_states=False, load_lr_scheduler_states=False
                )

        metrics = speed_metrics("train", start_time, self.state.max_steps)
        if self._total_flos is not None:
            self.store_flos()
            metrics["total_flos"] = self.state.total_flos
        self.log(metrics)

        self.control = self.callback_handler.on_train_end(self.args, self.state, self.control)
        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()

        return TrainOutput(self.state.global_step, self._total_loss_scalar / self.state.global_step, metrics)

        Main training entry point.

        Args:
            model_path (:obj:`str`, `optional`):
                Local path to the model if the model to train has been instantiated from a local path. If present,
                training will resume from the optimizer/scheduler states loaded here.
            trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`):
                The trial run or the hyperparameter dictionary for hyperparameter search.
        
        The main difference between ours and Huggingface's original implementation is that we 
        also load model_args when reloading best checkpoints for evaluation.
        
simcse/trainers.py