cl-lmm/src/utils/trainer.py

# _________________________________________________________


from transformers.trainer import *
from transformers import (
    TrainingArguments,
)
from .args import ContiunalRegularizationArguments
from peft_library.regularizations import EWC, LWF
from torch.nn import CrossEntropyLoss


def ce_loss_func(outputs, labels, num_items_in_batch=None, **kwargs):
    logits = outputs.logits
    device = logits.device
    # Shift so that tokens < n predict n
    shift_logits = logits[..., :-1, :]
    shift_labels = labels[..., 1:].to(device)
    # Save memory
    masks = shift_labels != -100
    shift_logits = shift_logits[masks]
    shift_labels = shift_labels[masks]
    # Flatten the tokens
    loss_fct = CrossEntropyLoss(reduction="none")
    loss = loss_fct(shift_logits, shift_labels)

    if num_items_in_batch is None:
        loss = loss.mean()
    else:
        # compat transformers>=4.46
        loss = loss.sum() / num_items_in_batch
    return loss


class ContinualTrainer(Trainer):
    def __init__(
        self,
        model,
        args: TrainingArguments,
        data_collator,
        train_dataset,
        eval_dataset,
        accelerator,
        reg_args: ContiunalRegularizationArguments = None,
    ):
        self.accelerator = accelerator
        super().__init__(
            model,
            args,
            data_collator,
            train_dataset,
            eval_dataset,
            # compute_loss_func=ce_loss_func,
        )

        if reg_args.ewc_enable:
            self.ewc_lambda = reg_args.ewc_lambda
            from peft_library.regularizations.ewc import EWC

            self.EWC = EWC()
            # fisher = t

        if reg_args.lwf_enable:
            self.lwf_lambda = reg_args.lwf_lambda
            from peft_library.regularizations.lwf import LWF

            self.LWF = LWF()

    def create_accelerator_and_postprocess(self):

        if self.accelerator is not None:
            self.is_deepspeed_enabled = (
                getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
            )
            self.is_fsdp_enabled = (
                getattr(self.accelerator.state, "fsdp_plugin", None) is not None
            )
            self.gather_function = self.accelerator.gather_for_metrics
            return
        else:
            super().create_accelerator_and_postprocess()
        
    def create_optimizer(self):
        """
        Setup the optimizer.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
        """
        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model

        if self.optimizer is None:
            decay_parameters = self.get_decay_parameter_names(opt_model)
            optimizer_grouped_parameters = [
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad and "merger" not in n)
                    ],
                    "weight_decay": self.args.weight_decay,
                    "lr": self.args.learning_rate,
                },
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad and "merger" in n)
                    ],
                    "weight_decay": self.args.weight_decay,
                    "lr": self.args.learning_rate / 10,
                },
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                    "lr": self.args.learning_rate,
                },
            ]

            if self.optimizer_cls_and_kwargs is not None:
                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
            else:
                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(self.args, opt_model)

            # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
            # e.g. for GaLore optimizer.
            if "params" in optimizer_kwargs:
                optimizer_grouped_parameters = optimizer_kwargs.pop("params")

            # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
            # e.g. for LOMO optimizer.
            if "model" in optimizer_kwargs:
                optimizer_grouped_parameters = optimizer_kwargs.pop("model")

            # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
            # to avoid arguments conflicts.
            if "optimizer_dict" in optimizer_kwargs:
                optimizer_grouped_parameters = optimizer_kwargs.pop("optimizer_dict")

            self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)

            if "bitsandbytes" in str(optimizer_cls) and optimizer_kwargs.get("optim_bits", None) == 8:
                import bitsandbytes

                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

                skipped = 0
                for module in opt_model.modules():
                    if isinstance(module, nn.Embedding):
                        skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
                        logger.info(f"skipped {module}: {skipped / 2**20}M params")
                        manager.register_module_override(module, "weight", {"optim_bits": 32})
                        logger.debug(f"bitsandbytes: will optimize {module} in fp32")
                logger.info(f"skipped: {skipped / 2**20}M params")

        if is_sagemaker_mp_enabled():
            self.optimizer = smp.DistributedOptimizer(self.optimizer)

        return self.optimizer