DistributedTrainer.py

from transformers.trainer import *

class DistributedTrainer(Trainer):
    def _inner_training_loop(
        self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
    ):
        self.accelerator.free_memory()
        self._train_batch_size = batch_size
        if self.args.auto_find_batch_size:
            if self.state.train_batch_size != self._train_batch_size:
                from accelerate.utils import release_memory

                (self.model_wrapped,) = release_memory(self.model_wrapped)
                self.model_wrapped = self.model

                # Check for DeepSpeed *after* the initial pass and modify the config
                if self.is_deepspeed_enabled:
                    # Temporarily unset `self.args.train_batch_size`
                    original_bs = self.args.per_device_train_batch_size
                    self.args.per_device_train_batch_size = self._train_batch_size // max(1, self.args.n_gpu)
                    self.propagate_args_to_deepspeed(True)
                    self.args.per_device_train_batch_size = original_bs
            self.state.train_batch_size = self._train_batch_size
        logger.debug(f"Currently training with a batch size of: {self._train_batch_size}")
        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()
        if self.is_fsdp_xla_v2_enabled:
            train_dataloader = tpu_spmd_dataloader(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
        total_train_batch_size = self.get_total_train_batch_size(args)

        (
            num_train_epochs,
            num_update_steps_per_epoch,
            num_examples,
            num_train_samples,
            epoch_based,
            len_dataloader,
            max_steps,
        ) = self.set_initial_training_values(args, train_dataloader, total_train_batch_size)

        num_train_tokens = None
        if self.args.include_tokens_per_second:
            num_train_tokens = self.num_tokens(train_dataloader, None if epoch_based else max_steps)
            # If going by epochs, multiply tokens linearly
            if len_dataloader is not None and epoch_based:
                num_train_tokens *= args.num_train_epochs
            # Otherwise since its steps, we just multiply by grad accum
            else:
                num_train_tokens *= args.gradient_accumulation_steps

        if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
            if self.args.n_gpu > 1:
                # nn.DataParallel(model) replicates the model, creating new variables and module
                # references registered here no longer work on other gpus, breaking the module
                raise ValueError(
                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP"
                    " (torchrun or torch.distributed.launch (deprecated))."
                )
            else:
                DebugUnderflowOverflow(self.model)

        delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled

        # Can't delay optimizer creation when using FSDP2: https://github.com/huggingface/accelerate/blob/3f636d626063ffcf9a337c7d3624d61b7d187d59/src/accelerate/accelerator.py#L1404
        is_fsdp2 = self.is_fsdp_enabled and (getattr(self.accelerator.state.fsdp_plugin, "fsdp_version", 1) == 2)
        if is_fsdp2:
            delay_optimizer_creation = False

        # We need to reset the scheduler, as its parameters may be different on subsequent calls
        if self._created_lr_scheduler:
            self.lr_scheduler = None
            self._created_lr_scheduler = False

        if self.is_deepspeed_enabled:
            self.optimizer, self.lr_scheduler = deepspeed_init(self, num_training_steps=max_steps)

        if not delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState(
            stateful_callbacks=[
                cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
            ]
        )
        self.state.is_hyper_param_search = trial is not None
        self.state.train_batch_size = self._train_batch_size

        # Compute absolute values for logging, eval, and save if given as ratio
        self.state.compute_steps(args, max_steps)

        # Activate gradient checkpointing if needed
        if args.gradient_checkpointing:
            self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=args.gradient_checkpointing_kwargs)

        model = self._wrap_model(self.model_wrapped)

        # as the model is wrapped, don't use `accelerator.prepare`
        # this is for unhandled cases such as
        # FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX
        use_accelerator_prepare = model is self.model

        if use_accelerator_prepare and self.is_fsdp_enabled:
            # In case of auto_find_batch_size=True
            # Remove FSDP wrapping from sub-models.
            self.model = unwrap_model(self.model, recursive=True)

        if delay_optimizer_creation:
            if use_accelerator_prepare:
                # configure fsdp plugin for qlora if any
                self._fsdp_qlora_plugin_updates()
                if self.accelerator.mixed_precision != "fp8":
                    self.model = self.accelerator.prepare(self.model)
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        # prepare using `accelerator` prepare
        use_accelerator_prepare = False
        if use_accelerator_prepare:
            self.model.train()
            if hasattr(self.lr_scheduler, "step"):
                if self.use_apex:
                    model = self.accelerator.prepare(self.model)
                else:
                    # We should avoid accelerate preparing the model in TP case since we dont need it as it is handled by transformers from_pretrained and also it goes into DDP based preparation.
                    if self.is_tp_enabled:
                        self.optimizer = self.accelerator.prepare(self.optimizer)
                    else:
                        model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
            else:
                # to handle cases wherein we pass "DummyScheduler" such as when it is specified in DeepSpeed config.
                model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
                    self.model, self.optimizer, self.lr_scheduler
                )
        else:
            self.optimizer = self.accelerator.prepare(self.optimizer)

        if self.is_fsdp_enabled:
            self.model = self.model_wrapped = model

        # 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

        # backward compatibility
        if self.is_deepspeed_enabled:
            self.deepspeed = self.model_wrapped

        # ckpt loading
        if resume_from_checkpoint is not None:
            if self.is_deepspeed_enabled:
                deepspeed_load_checkpoint(
                    self.model_wrapped, resume_from_checkpoint, load_module_strict=not _is_peft_model(self.model)
                )
            elif is_sagemaker_mp_enabled() or self.is_fsdp_enabled:
                self._load_from_checkpoint(resume_from_checkpoint, self.model_wrapped)

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

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

        # Train!
        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:,}")
        if self.args.per_device_train_batch_size != self._train_batch_size:
            logger.info(f"  Training with DataParallel so batch size has been adjusted to: {self._train_batch_size:,}")
        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size:,}")
        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
        logger.info(f"  Total optimization steps = {max_steps:,}")
        logger.info(f"  Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}")

        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 resume_from_checkpoint is not None and os.path.isfile(
            os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
        ):
            self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
            self.compare_trainer_and_checkpoint_args(self.args, self.state)
            self._load_callback_state()
            epochs_trained = int(self.state.global_step // num_update_steps_per_epoch)
            if not args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
                steps_trained_in_current_epoch *= 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 args.ignore_data_skip:
                logger.info(
                    f"  Will skip the first {epochs_trained} epochs then the first"
                    f" {steps_trained_in_current_epoch} batches in the first epoch."
                )

        # Update the references
        for attr in ("model", "optimizer", "lr_scheduler"):
            setattr(self.callback_handler, attr, getattr(self, attr))
        self.callback_handler.train_dataloader = train_dataloader

        self.state.init_training_references(self, max_steps, num_train_epochs, trial)

        # tr_loss is a tensor to avoid synchronization of TPUs through .item()
        tr_loss = torch.tensor(0.0, device=model.out_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 = self.state.global_step
        model.zero_grad()
        grad_norm: Optional[float] = None
        learning_rate = None
        self.control = self.callback_handler.on_train_begin(args, self.state, self.control)

        if args.eval_on_start:
            self._evaluate(trial, ignore_keys_for_eval, skip_scheduler=True)

        for epoch in range(epochs_trained, num_train_epochs):
            epoch_dataloader = train_dataloader
            if hasattr(epoch_dataloader, "set_epoch"):
                epoch_dataloader.set_epoch(epoch)

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

            steps_in_epoch = (
                len(epoch_dataloader)
                if len_dataloader is not None
                else args.max_steps * args.gradient_accumulation_steps
            )
            self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)

            step = -1
            rng_to_sync = False

            # Handle resumption from checkpoint
            if epoch == epochs_trained and resume_from_checkpoint is not None:
                if steps_trained_in_current_epoch > 0 and not args.ignore_data_skip:
                    epoch_dataloader = skip_first_batches(epoch_dataloader, steps_trained_in_current_epoch)
                    step = steps_trained_in_current_epoch - 1
                    rng_to_sync = True
                elif steps_trained_in_current_epoch == 0:
                    self._load_rng_state(resume_from_checkpoint)

            epoch_iterator = iter(epoch_dataloader)
            # We chunkify the epoch iterator into gradient accumulation steps `n` batches
            remainder = steps_in_epoch % args.gradient_accumulation_steps
            if remainder == 0:
                remainder = args.gradient_accumulation_steps
            update_step = -1
            total_updates = steps_in_epoch // args.gradient_accumulation_steps + int(
                remainder < args.gradient_accumulation_steps
            )
            for _ in range(total_updates):
                update_step += 1
                num_batches = args.gradient_accumulation_steps if update_step != (total_updates - 1) else remainder
                batch_samples, num_items_in_batch = self.get_batch_samples(epoch_iterator, num_batches, args.device)
                # Store the number of batches for current gradient accumulation
                # This is used to correctly scale the loss when the last accumulation step has fewer batches
                self.current_gradient_accumulation_steps = len(batch_samples)
                for i, inputs in enumerate(batch_samples):
                    step += 1
                    do_sync_step = (step + 1) % args.gradient_accumulation_steps == 0 or (step + 1) == steps_in_epoch
                    # Since we perform prefetching, we need to manually set sync_gradients
                    self.accelerator.gradient_state._set_sync_gradients(do_sync_step)

                    if self.args.include_num_input_tokens_seen not in ["no", False]:
                        main_input_name = getattr(self.model, "main_input_name", "input_ids")
                        if main_input_name not in inputs:
                            logger.warning(
                                "Tried to track the number of tokens seen, however the current model is "
                                "not configured properly to know what item is the input. To fix this, add "
                                "a `main_input_name` attribute to the model class you are using."
                            )
                        else:
                            if self.args.include_num_input_tokens_seen == "non_padding":
                                if "attention_mask" in inputs:
                                    input_tokens = inputs["attention_mask"].sum()
                                elif (
                                    self.processing_class is not None
                                    and hasattr(self.processing_class, "pad_token_id")
                                    and self.processing_class.pad_token_id is not None
                                ):
                                    input_tokens = (
                                        inputs[main_input_name] != self.processing_class.pad_token_id
                                    ).sum()
                                else:
                                    logger.warning(
                                        "Could not determine method to count non-padding tokens, falling back to counting all tokens."
                                    )
                                    input_tokens = inputs[main_input_name].numel()
                            else:
                                input_tokens = inputs[main_input_name].numel()

                            input_tokens = torch.tensor(input_tokens, device=self.args.device, dtype=torch.int64)
                            self.state.num_input_tokens_seen += self.accelerator.gather(input_tokens).sum().item()

                    if rng_to_sync:
                        self._load_rng_state(resume_from_checkpoint)
                        rng_to_sync = False

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

                    # We explicitly want to avoid relying on `accelerator.accumulate` for generation training
                    context = (
                        functools.partial(self.accelerator.no_sync, model=model)
                        if i != len(batch_samples) - 1
                        and self.accelerator.distributed_type != DistributedType.DEEPSPEED
                        else contextlib.nullcontext
                    )
                    with context():
                        tr_loss_step = self.training_step(model, inputs, num_items_in_batch)

                    if (
                        args.logging_nan_inf_filter
                        and not is_torch_xla_available()
                        and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
                    ):
                        # if loss is nan or inf simply add the average of previous logged losses
                        tr_loss = tr_loss + tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
                    else:
                        if tr_loss.device != tr_loss_step.device:
                            raise ValueError(
                                f"Calculated loss must be on the original device: {tr_loss.device} but device in use is {tr_loss_step.device}"
                            )
                        tr_loss = tr_loss + tr_loss_step

                    self.current_flos += float(self.floating_point_ops(inputs))

                    if do_sync_step:
                        # Since we perform prefetching, we need to manually set sync_gradients to True
                        self.accelerator.gradient_state._set_sync_gradients(True)

                        # Gradient clipping
                        if args.max_grad_norm is not None and args.max_grad_norm > 0:
                            if is_sagemaker_mp_enabled() and args.fp16:
                                _grad_norm = self.optimizer.clip_master_grads(args.max_grad_norm)
                            elif self.use_apex:
                                from apex import amp

                                # Revert to normal clipping otherwise, handling Apex or full precision
                                _grad_norm = nn.utils.clip_grad_norm_(
                                    amp.master_params(self.optimizer),
                                    args.max_grad_norm,
                                )
                            else:
                                grad_norm_context = contextlib.nullcontext
                                if self.is_tp_enabled:
                                    from torch.distributed._tensor.experimental import implicit_replication

                                    grad_norm_context = implicit_replication
                                with grad_norm_context():
                                    _grad_norm = self.accelerator.clip_grad_norm_(
                                        model.parameters(),
                                        args.max_grad_norm,
                                    )

                            if (
                                is_accelerate_available()
                                and self.accelerator.distributed_type == DistributedType.DEEPSPEED
                            ):
                                grad_norm = model.get_global_grad_norm()
                                # In some cases the grad norm may not return a float
                                if hasattr(grad_norm, "item"):
                                    grad_norm = grad_norm.item()
                            else:
                                grad_norm = _grad_norm

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

                        context = contextlib.nullcontext
                        if self.is_tp_enabled:
                            from torch.distributed._tensor.experimental import implicit_replication

                            context = implicit_replication

                        with context():
                            self.optimizer.step()

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

                        # get leaning rate before update
                        learning_rate = self._get_learning_rate()

                        if not self.accelerator.optimizer_step_was_skipped:
                            # Delay optimizer scheduling until metrics are generated
                            if not isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
                                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(args, self.state, self.control)
                        self._maybe_log_save_evaluate(
                            tr_loss,
                            grad_norm,
                            model,
                            trial,
                            epoch,
                            ignore_keys_for_eval,
                            start_time,
                            learning_rate=learning_rate,
                        )
                    else:
                        self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                    # PyTorch/XLA relies on the data loader to insert the mark_step for
                    # each step. Since we are breaking the loop early, we need to manually
                    # insert the mark_step here.
                    if self.control.should_epoch_stop or self.control.should_training_stop:
                        if is_torch_xla_available():
                            xm.mark_step()
                        break
                # We also need to break out of the nested loop
                if self.control.should_epoch_stop or self.control.should_training_stop:
                    if is_torch_xla_available():
                        xm.mark_step()
                    break
            if step < 0:
                logger.warning(
                    "There seems not to be a single sample in your epoch_iterator, stopping training at step"
                    f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
                    f" num_steps ({max_steps}) higher than the number of available samples."
                )
                self.control.should_training_stop = True

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

            if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                if is_torch_xla_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 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 args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            self._load_best_model()

        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()
        effective_global_step = max(self.state.global_step, 0.001)  # Avoid ZeroDivisionError
        train_loss = self._total_loss_scalar / effective_global_step

        metrics = speed_metrics(
            "train",
            start_time,
            num_samples=num_train_samples,
            num_steps=self.state.max_steps,
            num_tokens=num_train_tokens,
        )
        self.store_flos()
        metrics["total_flos"] = self.state.total_flos
        metrics["train_loss"] = train_loss

        self.is_in_train = False

        self._memory_tracker.stop_and_update_metrics(metrics)

        self.log(metrics)

        run_dir = self._get_output_dir(trial)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir)

        # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.
        if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:
            for checkpoint in checkpoints_sorted:
                if not os.path.samefile(checkpoint, self.state.best_model_checkpoint):
                    logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
                    shutil.rmtree(checkpoint, ignore_errors=True)

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

        # Wait for the checkpoint to be uploaded.
        self._finish_current_push()

        # After training we make sure to retrieve back the original forward pass method
        # for the embedding layer by removing the forward post hook.
        if self.neftune_noise_alpha is not None:
            self._deactivate_neftune(self.model)

        return TrainOutput(self.state.global_step, train_loss, metrics)