experiments/exp_base.py

"""
This repo is forked from [Boyuan Chen](https://boyuan.space/)'s research 
template [repo](https://github.com/buoyancy99/research-template). 
By its MIT license, you must keep the above sentence in `README.md` 
and the `LICENSE` file to credit the author.
"""

from abc import ABC, abstractmethod
from typing import Optional, Union, Literal, List, Dict
import pathlib
import os

import hydra
import torch
from lightning.pytorch.strategies.ddp import DDPStrategy

import lightning.pytorch as pl
from lightning.pytorch.loggers.wandb import WandbLogger
from lightning.pytorch.utilities.types import TRAIN_DATALOADERS
from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_info

from omegaconf import DictConfig

from utils.print_utils import cyan
from utils.distributed_utils import is_rank_zero
from safetensors.torch import load_file, load_model
from pathlib import Path
from huggingface_hub import hf_hub_download
from huggingface_hub import model_info

torch.set_float32_matmul_precision("high")

def is_huggingface_model(path: str) -> bool:
    hf_ckpt = str(path).split('/')
    repo_id = '/'.join(hf_ckpt[:2])
    try:
        model_info(repo_id)
        return True
    except:
        return False
    
def _extract_state_dict(checkpoint):
    if isinstance(checkpoint, dict) and isinstance(checkpoint.get("state_dict"), dict):
        return checkpoint["state_dict"]
    return checkpoint


def _compatible_state_dict(algo, state_dict):
    model_state = algo.state_dict()
    prefixes = (
        "",
        "model.",
        "module.",
        "algo.",
        "diffusion_model.",
        "diffusion_model.model.",
        "vae.",
    )
    best = (
        {},
        {
            "matched": 0,
            "missing": 0,
            "unexpected": 0,
            "skipped_prefix": 0,
            "shape_mismatch": 0,
            "missing_model": len(model_state),
            "total": len(state_dict),
            "prefix": "",
            "unexpected_checkpoint_keys": [],
            "shape_mismatch_keys": [],
            "missing_model_keys": list(model_state.keys()),
        },
    )
    for prefix in prefixes:
        compatible = {}
        unexpected_checkpoint_keys = []
        shape_mismatch_keys = []
        skipped_prefix = 0
        for key, value in state_dict.items():
            if key in ["data_mean", "data_std"]:
                continue
            if prefix and not key.startswith(prefix):
                skipped_prefix += 1
                continue
            stripped = key.removeprefix(prefix)
            if stripped not in model_state:
                unexpected_checkpoint_keys.append(key)
                continue
            if hasattr(value, "shape") and value.shape != model_state[stripped].shape:
                shape_mismatch_keys.append(
                    (
                        key,
                        stripped,
                        tuple(value.shape),
                        tuple(model_state[stripped].shape),
                    )
                )
                continue
            compatible[stripped] = value
        missing_model_keys = [key for key in model_state.keys() if key not in compatible]
        if len(compatible) > best[1]["matched"]:
            best = (
                compatible,
                {
                    "matched": len(compatible),
                    "missing": skipped_prefix + len(unexpected_checkpoint_keys),
                    "unexpected": len(unexpected_checkpoint_keys),
                    "skipped_prefix": skipped_prefix,
                    "shape_mismatch": len(shape_mismatch_keys),
                    "missing_model": len(missing_model_keys),
                    "total": len(state_dict),
                    "prefix": prefix,
                    "unexpected_checkpoint_keys": unexpected_checkpoint_keys,
                    "shape_mismatch_keys": shape_mismatch_keys,
                    "missing_model_keys": missing_model_keys,
                },
            )
    return best


def _key_matches_any(key: str, markers: tuple[str, ...]) -> bool:
    return any(marker in key for marker in markers)


def _format_key_samples(keys, limit: int = 25, indent: str = "    ") -> str:
    sample = list(keys[:limit])
    lines = [f"{indent}{key}" for key in sample]
    if len(keys) > limit:
        lines.append(f"{indent}... {len(keys) - limit} more")
    return "\n".join(lines)


def _format_shape_mismatch_samples(shape_mismatch_keys, limit: int = 25, indent: str = "    ") -> str:
    sample = list(shape_mismatch_keys[:limit])
    lines = [
        f"{indent}{checkpoint_key} -> {model_key}: checkpoint{checkpoint_shape} != model{model_shape}"
        for checkpoint_key, model_key, checkpoint_shape, model_shape in sample
    ]
    if len(shape_mismatch_keys) > limit:
        lines.append(f"{indent}... {len(shape_mismatch_keys) - limit} more")
    return "\n".join(lines)


def _log_checkpoint_mismatch_report(
    stats,
    checkpoint_path,
    label: str | None = None,
    dememwm_key_check: bool = False,
) -> None:
    mismatch_count = stats["missing_model"] + stats["unexpected"] + stats["shape_mismatch"]
    if mismatch_count == 0:
        return

    title = label or str(checkpoint_path)
    lines = [
        f"Checkpoint mismatch report for {title}:",
        f"  checkpoint={checkpoint_path}",
        "  selected_prefix={!r}".format(stats["prefix"]),
        (
            "  counts: "
            "matched={matched} "
            "model_not_loaded={model_not_loaded} "
            "checkpoint_not_used={checkpoint_not_used} "
            "shape_mismatch={shape_mismatch} "
            "skipped_by_prefix={skipped_by_prefix}"
        ).format(
            matched=stats["matched"],
            model_not_loaded=stats["missing_model"],
            checkpoint_not_used=stats["unexpected"],
            shape_mismatch=stats["shape_mismatch"],
            skipped_by_prefix=stats["skipped_prefix"],
        ),
    ]
    if stats["missing_model_keys"]:
        lines.append("  Model keys not loaded from checkpoint:")
        lines.append(_format_key_samples(stats["missing_model_keys"]))
    if stats["unexpected_checkpoint_keys"]:
        lines.append("  Checkpoint keys not used by current model:")
        lines.append(_format_key_samples(stats["unexpected_checkpoint_keys"]))
    if stats["shape_mismatch_keys"]:
        lines.append("  Shape mismatches:")
        lines.append(_format_shape_mismatch_samples(stats["shape_mismatch_keys"]))

    if dememwm_key_check:
        markers = ("dememwm_", ".dememwm_", ".memory_token_cross_attn.")
        missing_dememwm = [key for key in stats["missing_model_keys"] if _key_matches_any(key, markers)]
        unexpected_dememwm = [
            key for key in stats["unexpected_checkpoint_keys"] if _key_matches_any(key, markers)
        ]
        shape_dememwm = [
            item
            for item in stats["shape_mismatch_keys"]
            if _key_matches_any(item[0], markers) or _key_matches_any(item[1], markers)
        ]
        if missing_dememwm or unexpected_dememwm or shape_dememwm:
            lines.append("  DeMemWM mismatch subset:")
            if missing_dememwm:
                lines.append("    DeMemWM model keys not loaded:")
                lines.append(_format_key_samples(missing_dememwm, indent="      "))
            if unexpected_dememwm:
                lines.append("    DeMemWM checkpoint keys not used:")
                lines.append(_format_key_samples(unexpected_dememwm, indent="      "))
            if shape_dememwm:
                lines.append("    DeMemWM shape mismatches:")
                lines.append(_format_shape_mismatch_samples(shape_dememwm, indent="      "))

    rank_zero_info("\n".join(lines))


def load_custom_checkpoint(
    algo,
    checkpoint_path,
    require_match: bool = False,
    label: str | None = None,
    dememwm_key_check: bool = False,
    report_key_mismatch: bool = False,
):
    if not checkpoint_path:
        if require_match:
            target = label or "model"
            raise FileNotFoundError(f"Expected checkpoint for {target}, but no path was provided.")
        rank_zero_info("No checkpoint path provided, skipping checkpoint loading.")
        return None

    if not isinstance(checkpoint_path, Path):
        checkpoint_path = Path(checkpoint_path)

    if is_huggingface_model(str(checkpoint_path)):
        hf_ckpt = str(checkpoint_path).split("/")
        repo_id = "/".join(hf_ckpt[:2])
        file_name = "/".join(hf_ckpt[2:])
        model_path = hf_hub_download(repo_id=repo_id, filename=file_name)
        ckpt = torch.load(model_path, map_location=torch.device("cpu"))
        state_dict = _extract_state_dict(ckpt)

    elif checkpoint_path.suffix == ".pt":
        ckpt = torch.load(checkpoint_path, weights_only=True)
        state_dict = _extract_state_dict(ckpt)

    elif checkpoint_path.suffix == ".ckpt":
        ckpt = torch.load(checkpoint_path, map_location=torch.device("cpu"))
        state_dict = _extract_state_dict(ckpt)

    elif checkpoint_path.suffix == ".safetensors":
        state_dict = load_file(checkpoint_path)

    elif os.path.isdir(checkpoint_path):
        ckpt_files = [f for f in os.listdir(checkpoint_path) if f.endswith(".ckpt")]
        if not ckpt_files:
            raise FileNotFoundError("No .ckpt files found in the specified directory!")
        selected_ckpt = max(ckpt_files)
        selected_ckpt_path = os.path.join(checkpoint_path, selected_ckpt)
        print(f"Checkpoint file selected for loading: {selected_ckpt_path}")

        ckpt = torch.load(selected_ckpt_path, map_location=torch.device("cpu"))
        state_dict = _extract_state_dict(ckpt)

    else:
        raise ValueError(f"Unsupported checkpoint: {checkpoint_path}")
    if dememwm_key_check and hasattr(algo, "strict_dememwm_checkpoint_key_check"):
        algo.strict_dememwm_checkpoint_key_check(state_dict)
    compatible, stats = _compatible_state_dict(algo, state_dict)
    if require_match and stats["matched"] == 0:
        raise RuntimeError(f"Expected checkpoint for {label or checkpoint_path} matched zero model weights.")
    if compatible:
        algo.load_state_dict(compatible, strict=False)
    elif checkpoint_path.suffix == ".safetensors":
        load_model(algo, checkpoint_path, strict=False)
    rank_zero_info(
        "Model weights loaded from {}: matched={} missing={} shape_mismatch={} prefix={!r}".format(
            checkpoint_path,
            stats["matched"],
            stats["missing"],
            stats["shape_mismatch"],
            stats["prefix"],
        )
    )
    if report_key_mismatch:
        _log_checkpoint_mismatch_report(stats, checkpoint_path, label=label, dememwm_key_check=dememwm_key_check)
    return stats


class BaseExperiment(ABC):
    """
    Abstract class for an experiment. This generalizes the pytorch lightning Trainer & lightning Module to more
    flexible experiments that doesn't fit in the typical ml loop, e.g. multi-stage reinforcement learning benchmarks.
    """

    # each key has to be a yaml file under '[project_root]/configurations/algorithm' without .yaml suffix
    compatible_algorithms: Dict = NotImplementedError

    def __init__(
        self,
        root_cfg: DictConfig,
        logger: Optional[WandbLogger] = None,
        ckpt_path: Optional[Union[str, pathlib.Path]] = None,
    ) -> None:
        """
        Constructor

        Args:
            cfg: configuration file that contains everything about the experiment
            logger: a pytorch-lightning WandbLogger instance
            ckpt_path: an optional path to saved checkpoint
        """
        super().__init__()
        self.root_cfg = root_cfg
        self.cfg = root_cfg.experiment
        self.debug = root_cfg.debug
        self.logger = logger
        self.ckpt_path = ckpt_path
        self.algo = None
        self.customized_load = getattr(root_cfg, "customized_load", False)
        self.seperate_load = getattr(root_cfg, "seperate_load", False)
        self.zero_init_gate= getattr(root_cfg, "zero_init_gate", False)
        self.only_tune_memory = getattr(root_cfg, "only_tune_memory", False)
        self.diffusion_model_path = getattr(root_cfg, "diffusion_model_path", None)
        self.vae_path = getattr(root_cfg, "vae_path", None)
        self.pose_predictor_path = getattr(root_cfg, "pose_predictor_path", None)
        self.auto_resuming = getattr(root_cfg, "_auto_resuming", False)

    def _build_algo(self):
        """
        Build the lightning module
        :return:  a pytorch-lightning module to be launched
        """
        algo_name = self.root_cfg.algorithm._name
        if algo_name not in self.compatible_algorithms:
            raise ValueError(
                f"Algorithm {algo_name} not found in compatible_algorithms for this Experiment class. "
                "Make sure you define compatible_algorithms correctly and make sure that each key has "
                "same name as yaml file under '[project_root]/configurations/algorithm' without .yaml suffix"
            )
        return self.compatible_algorithms[algo_name](self.root_cfg.algorithm)

    def exec_task(self, task: str) -> None:
        """
        Executing a certain task specified by string. Each task should be a stage of experiment.
        In most computer vision / nlp applications, tasks should be just train and test.
        In reinforcement learning, you might have more stages such as collecting dataset etc

        Args:
            task: a string specifying a task implemented for this experiment
        """
        if hasattr(self, task) and callable(getattr(self, task)):
            if is_rank_zero:
                print(cyan("Executing task:"), f"{task} out of {self.cfg.tasks}")
            getattr(self, task)()
        else:
            raise ValueError(
                f"Specified task '{task}' not defined for class {self.__class__.__name__} or is not callable."
            )

    def exec_interactive(self, task: str) -> None:
        """
        Executing a certain task specified by string. Each task should be a stage of experiment.
        In most computer vision / nlp applications, tasks should be just train and test.
        In reinforcement learning, you might have more stages such as collecting dataset etc

        Args:
            task: a string specifying a task implemented for this experiment
        """
        if hasattr(self, task) and callable(getattr(self, task)):
            if is_rank_zero:
                print(cyan("Executing task:"), f"{task} out of {self.cfg.tasks}")
            return getattr(self, task)()
        else:
            raise ValueError(
                f"Specified task '{task}' not defined for class {self.__class__.__name__} or is not callable."
            )

class BaseLightningExperiment(BaseExperiment):
    """
    Abstract class for pytorch lightning experiments. Useful for computer vision & nlp where main components are
    simply models, datasets and train loop.
    """

    # each key has to be a yaml file under '[project_root]/configurations/algorithm' without .yaml suffix
    compatible_algorithms: Dict = NotImplementedError

    # each key has to be a yaml file under '[project_root]/configurations/dataset' without .yaml suffix
    compatible_datasets: Dict = NotImplementedError

    def _build_trainer_callbacks(self):
        callbacks = []
        if self.logger:
            callbacks.append(LearningRateMonitor("step", True))

    def _build_training_loader(self) -> Optional[Union[TRAIN_DATALOADERS, pl.LightningDataModule]]:
        train_dataset = self._build_dataset("training")
        shuffle = (
            False if isinstance(train_dataset, torch.utils.data.IterableDataset) else self.cfg.training.data.shuffle
        )
        if train_dataset:
            return torch.utils.data.DataLoader(
                train_dataset,
                batch_size=self.cfg.training.batch_size,
                num_workers=min(os.cpu_count(), self.cfg.training.data.num_workers),
                shuffle=shuffle,
                persistent_workers=True,
                pin_memory=torch.cuda.is_available(),
            )
        else:
            return None

    def _build_validation_loader(self) -> Optional[Union[TRAIN_DATALOADERS, pl.LightningDataModule]]:
        validation_dataset = self._build_dataset("validation")
        shuffle = (
            False
            if isinstance(validation_dataset, torch.utils.data.IterableDataset)
            else self.cfg.validation.data.shuffle
        )
        if validation_dataset:
            return torch.utils.data.DataLoader(
                validation_dataset,
                batch_size=self.cfg.validation.batch_size,
                num_workers=min(os.cpu_count(), self.cfg.validation.data.num_workers),
                shuffle=shuffle,
                persistent_workers=True,
                pin_memory=torch.cuda.is_available(),
            )
        else:
            return None

    def _build_test_loader(self) -> Optional[Union[TRAIN_DATALOADERS, pl.LightningDataModule]]:
        test_dataset = self._build_dataset("test")
        shuffle = False if isinstance(test_dataset, torch.utils.data.IterableDataset) else self.cfg.test.data.shuffle
        if test_dataset:
            return torch.utils.data.DataLoader(
                test_dataset,
                batch_size=self.cfg.test.batch_size,
                num_workers=min(os.cpu_count(), self.cfg.test.data.num_workers),
                shuffle=shuffle,
                persistent_workers=True,
                pin_memory=torch.cuda.is_available(),
            )
        else:
            return None

    def training(self) -> None:
        """
        All training happens here
        """
        if not self.algo:
            self.algo = self._build_algo()
        if self.cfg.training.compile:
            self.algo = torch.compile(self.algo)

        callbacks = []
        if self.logger:
            callbacks.append(LearningRateMonitor("step", True))
        if "checkpointing" in self.cfg.training:
            callbacks.append(
                ModelCheckpoint(
                    pathlib.Path(hydra.core.hydra_config.HydraConfig.get()["runtime"]["output_dir"]) / "checkpoints",
                    filename="epoch{epoch}_step{step}",
                    auto_insert_metric_name=False,
                    **self.cfg.training.checkpointing,
                )
            )

        trainer = pl.Trainer(
            accelerator="auto",
            devices="auto",
            strategy=DDPStrategy(find_unused_parameters=True) if torch.cuda.device_count() > 1 else "auto",
            logger=self.logger or False,
            callbacks=callbacks,
            gradient_clip_val=self.cfg.training.optim.gradient_clip_val or 0.0,
            val_check_interval=self.cfg.validation.val_every_n_step if self.cfg.validation.val_every_n_step else None,
            limit_val_batches=self.cfg.validation.limit_batch,
            check_val_every_n_epoch=self.cfg.validation.val_every_n_epoch if not self.cfg.validation.val_every_n_step else None,
            accumulate_grad_batches=self.cfg.training.optim.accumulate_grad_batches or 1,
            precision=self.cfg.training.precision or 32,
            detect_anomaly=False,
            num_sanity_val_steps=int(self.cfg.debug) if self.cfg.debug else 0,
            max_epochs=self.cfg.training.max_epochs,
            max_steps=self.cfg.training.max_steps,
            max_time=self.cfg.training.max_time
        )


        if self.auto_resuming:
            self.algo._strict_resume_state = True
            trainer.fit(
                self.algo,
                train_dataloaders=self._build_training_loader(),
                val_dataloaders=self._build_validation_loader(),
                ckpt_path=self.ckpt_path,
            )
        elif self.customized_load:
            if self.seperate_load:
                if 'oasis500m' in self.diffusion_model_path:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model.model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model.model",
                    )
                else:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model",
                    )
                load_custom_checkpoint(algo=self.algo.vae, checkpoint_path=self.vae_path, require_match=True, label="vae")
            else:
                load_custom_checkpoint(algo=self.algo, checkpoint_path=self.ckpt_path, dememwm_key_check=True)

            if self.zero_init_gate:
                for name, para in self.algo.diffusion_model.named_parameters():
                    if 'r_adaLN_modulation' in name:
                        para.requires_grad_(False)
                        para[2*1024:3*1024] = 0
                        para[5*1024:6*1024] = 0
                        para.requires_grad_(True)

            if self.only_tune_memory:
                for name, para in self.algo.diffusion_model.named_parameters():
                    para.requires_grad_(False)
                    if 'r_' in name or 'pose_embedder' in name or 'pose_cond_mlp' in name or 'lora_' in name:
                        para.requires_grad_(True)
                    
            trainer.fit(
                self.algo,
                train_dataloaders=self._build_training_loader(),
                val_dataloaders=self._build_validation_loader(),
                ckpt_path=None,
            )
        else:

            if self.only_tune_memory:
                for name, para in self.algo.diffusion_model.named_parameters():
                    para.requires_grad_(False)
                    if 'r_' in name or 'pose_embedder' in name or 'pose_cond_mlp' in name or 'lora_' in name:
                        para.requires_grad_(True)
            
            trainer.fit(
                self.algo,
                train_dataloaders=self._build_training_loader(),
                val_dataloaders=self._build_validation_loader(),
                ckpt_path=self.ckpt_path,
            )

    def validation(self) -> None:
        """
        All validation happens here
        """
        if not self.algo:
            self.algo = self._build_algo()
        if self.cfg.validation.compile:
            self.algo = torch.compile(self.algo)

        callbacks = []

        trainer = pl.Trainer(
            accelerator="auto",
            logger=self.logger,
            devices="auto",
            num_nodes=self.cfg.num_nodes,
            strategy=DDPStrategy(find_unused_parameters=False) if torch.cuda.device_count() > 1 else "auto",
            callbacks=callbacks,
            limit_val_batches=self.cfg.validation.limit_batch,
            precision=self.cfg.validation.precision,
            detect_anomaly=False,  # self.cfg.debug,
            inference_mode=self.cfg.validation.inference_mode,
        )

        if self.customized_load:
            if self.seperate_load:
                if 'oasis500m' in self.diffusion_model_path:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model.model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model.model",
                        report_key_mismatch=True,
                    )
                else:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model",
                        report_key_mismatch=True,
                    )
                load_custom_checkpoint(
                    algo=self.algo.vae,
                    checkpoint_path=self.vae_path,
                    require_match=True,
                    label="vae",
                    report_key_mismatch=True,
                )
            else:
                load_custom_checkpoint(
                    algo=self.algo,
                    checkpoint_path=self.ckpt_path,
                    label="algo",
                    dememwm_key_check=True,
                    report_key_mismatch=True,
                )

            if self.zero_init_gate:
                for name, para in self.algo.diffusion_model.named_parameters():
                    if 'r_adaLN_modulation' in name:
                        para.requires_grad_(False)
                        para[2*1024:3*1024] = 0
                        para[5*1024:6*1024] = 0
                        para.requires_grad_(True)
            
            trainer.validate(
                self.algo,
                dataloaders=self._build_validation_loader(),
                ckpt_path=None,
            )
        else:
            trainer.validate(
                self.algo,
                dataloaders=self._build_validation_loader(),
                ckpt_path=self.ckpt_path,
            )

    def test(self) -> None:
        """
        All testing happens here
        """
        if not self.algo:
            self.algo = self._build_algo()
        if self.cfg.test.compile:
            self.algo = torch.compile(self.algo)

        callbacks = []

        trainer = pl.Trainer(
            accelerator="auto",
            logger=self.logger,
            devices="auto",
            num_nodes=self.cfg.num_nodes,
            strategy=DDPStrategy(find_unused_parameters=False) if torch.cuda.device_count() > 1 else "auto",
            callbacks=callbacks,
            limit_test_batches=self.cfg.test.limit_batch,
            precision=self.cfg.test.precision,
            detect_anomaly=False,  # self.cfg.debug,
        )

        if self.customized_load:
            if self.seperate_load:
                if 'oasis500m' in self.diffusion_model_path:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model.model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model.model",
                        report_key_mismatch=True,
                    )
                else:
                    load_custom_checkpoint(
                        algo=self.algo.diffusion_model,
                        checkpoint_path=self.diffusion_model_path,
                        require_match=True,
                        label="diffusion_model",
                        report_key_mismatch=True,
                    )
                load_custom_checkpoint(
                    algo=self.algo.vae,
                    checkpoint_path=self.vae_path,
                    require_match=True,
                    label="vae",
                    report_key_mismatch=True,
                )
            else:
                load_custom_checkpoint(
                    algo=self.algo,
                    checkpoint_path=self.ckpt_path,
                    label="algo",
                    dememwm_key_check=True,
                    report_key_mismatch=True,
                )

            if self.zero_init_gate:
                for name, para in self.algo.diffusion_model.named_parameters():
                    if 'r_adaLN_modulation' in name:
                        para.requires_grad_(False)
                        para[2*1024:3*1024] = 0
                        para[5*1024:6*1024] = 0
                        para.requires_grad_(True)
            
            trainer.test(
                self.algo,
                dataloaders=self._build_test_loader(),
                ckpt_path=None,
            )
        else:
            trainer.test(
                self.algo,
                dataloaders=self._build_test_loader(),
                ckpt_path=self.ckpt_path,
            )

    def _build_dataset(self, split: str) -> Optional[torch.utils.data.Dataset]:
        if split in ["training", "test", "validation"]:
            return self.compatible_datasets[self.root_cfg.dataset._name](self.root_cfg.dataset, split=split)
        else:
            raise NotImplementedError(f"split '{split}' is not implemented")