training.py

# SPDX-License-Identifier: LGPL-3.0-or-later
import datetime
import functools
import json
import logging
import time
from collections.abc import (
    Callable,
    Generator,
    Iterable,
)
from contextlib import (
    nullcontext,
)
from copy import (
    deepcopy,
)
from pathlib import (
    Path,
)
from typing import (
    Any,
)

import numpy as np
import torch

from deepmd.common import (
    symlink_prefix_files,
)
from deepmd.dpmodel.utils import (
    compute_total_numb_batch,
    resolve_model_prob,
    resolve_model_prob_from_epochs,
)
from deepmd.loggers.training import (
    format_training_message,
    format_training_message_per_task,
)
from deepmd.pt.loss import (
    DenoiseLoss,
    DOSLoss,
    EnergyHessianStdLoss,
    EnergySpinLoss,
    EnergyStdLoss,
    PropertyLoss,
    TaskLoss,
    TensorLoss,
)
from deepmd.pt.model.model import (
    get_model,
    get_zbl_model,
)
from deepmd.pt.optimizer import (
    AdaMuonOptimizer,
    HybridMuonOptimizer,
    KFOptimizerWrapper,
    LKFOptimizer,
)
from deepmd.pt.train.ema import (
    EMA_CHECKPOINT_KEY,
    ModelEMA,
    get_ema_checkpoint_prefix,
    get_ema_validation_log_path,
)
from deepmd.pt.train.validation import (
    FullValidator,
    resolve_full_validation_start_step,
)
from deepmd.pt.train.wrapper import (
    ModelWrapper,
)
from deepmd.pt.utils import (
    dp_random,
)
from deepmd.pt.utils.dataloader import (
    DpLoaderSet,
    get_sampler_from_params,
)
from deepmd.pt.utils.env import (
    DEVICE,
    JIT,
    LOCAL_RANK,
    NUM_WORKERS,
    SAMPLER_RECORD,
)
from deepmd.pt.utils.learning_rate import (
    BaseLR,
)
from deepmd.pt.utils.lmdb_dataset import (
    LmdbDataset,
    _collate_lmdb_batch,
    _SameNlocBatchSamplerTorch,
)
from deepmd.pt.utils.stat import (
    make_stat_input,
)
from deepmd.pt.utils.utils import (
    to_numpy_array,
)
from deepmd.utils.data import (
    DataRequirementItem,
)

if torch.__version__.startswith("2"):
    import torch._dynamo

import torch.distributed as dist
from torch.distributed.checkpoint.state_dict import (
    StateDictOptions,
    get_model_state_dict,
    get_optimizer_state_dict,
    set_optimizer_state_dict,
)

try:
    from torch.distributed.fsdp import (
        fully_shard,
    )
except ImportError:
    fully_shard = None  # type: ignore[assignment]
from torch.distributed.optim import (
    ZeroRedundancyOptimizer,
)
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import (
    DataLoader,
)

from deepmd.utils.path import (
    DPH5Path,
)

log = logging.getLogger(__name__)


class Trainer:
    def __init__(
        self,
        config: dict[str, Any],
        training_data: DpLoaderSet,
        stat_file_path: str | None = None,
        validation_data: DpLoaderSet | None = None,
        init_model: str | None = None,
        restart_model: str | None = None,
        finetune_model: str | None = None,
        force_load: bool = False,
        shared_links: dict[str, str] | None = None,
        finetune_links: dict[str, str] | None = None,
        init_frz_model: str | None = None,
    ) -> None:
        """Construct a DeePMD trainer.

        Args:
        - config: The Dict-like configuration with training options.
        """
        if init_model is not None:
            resume_model = init_model
        elif restart_model is not None:
            resume_model = restart_model
        elif finetune_model is not None:
            resume_model = finetune_model
        else:
            resume_model = None
        resuming = resume_model is not None
        self.restart_training = restart_model is not None
        model_params = config["model"]
        training_params = config["training"]
        optimizer_params = config.get("optimizer", {})
        self.multi_task = "model_dict" in model_params
        self.finetune_links = finetune_links
        self.finetune_update_stat = False
        self.model_keys = (
            list(model_params["model_dict"]) if self.multi_task else ["Default"]
        )
        self.is_distributed = dist.is_available() and dist.is_initialized()
        self.rank = dist.get_rank() if self.is_distributed else 0
        self.world_size = dist.get_world_size() if self.is_distributed else 1
        self.num_model = len(self.model_keys)
        self.model_prob = None

        # Iteration config
        self.num_steps = training_params.get("numb_steps")
        self.num_epoch = training_params.get("numb_epoch")
        self.num_epoch_dict = training_params.get("num_epoch_dict")
        self.disp_file = training_params.get("disp_file", "lcurve.out")
        self.disp_freq = training_params.get("disp_freq", 1000)
        self.disp_avg = training_params.get("disp_avg", False)
        self.save_ckpt = training_params.get("save_ckpt", "model.ckpt")
        self.save_freq = training_params.get("save_freq", 1000)
        self.max_ckpt_keep = training_params.get("max_ckpt_keep", 5)
        self.enable_ema = bool(training_params.get("enable_ema", False))
        self.ema_decay = float(training_params.get("ema_decay", 0.999))
        self.ema_ckpt_keep = int(training_params.get("ema_ckpt_keep", 3))
        self.ema_save_ckpt = get_ema_checkpoint_prefix(self.save_ckpt)
        self.display_in_training = training_params.get("disp_training", True)
        self.timing_in_training = training_params.get("time_training", True)
        self.change_bias_after_training = training_params.get(
            "change_bias_after_training", False
        )
        self.zero_stage = int(training_params.get("zero_stage", 0))
        if self.zero_stage not in (0, 1, 2, 3):
            raise ValueError(
                f"training.zero_stage must be 0, 1, 2, or 3, got {self.zero_stage}"
            )
        if self.enable_ema and self.zero_stage >= 2:
            raise ValueError(
                "training.enable_ema currently only supports training.zero_stage < 2."
            )
        if self.zero_stage > 0 and not self.is_distributed:
            self.zero_stage = 0
        if self.zero_stage > 0 and self.change_bias_after_training:
            raise ValueError(
                "training.zero_stage does not support change_bias_after_training."
            )
        self.lcurve_should_print_header = True

        def get_opt_param(params: dict[str, Any]) -> tuple[str, dict[str, Any]]:
            """
            Extract optimizer parameters.

            Note: Default values are already filled by argcheck.normalize()
            before this function is called.
            """
            opt_type = params.get("type", "Adam")
            if opt_type not in ("Adam", "AdamW", "LKF", "AdaMuon", "HybridMuon"):
                raise ValueError(f"Not supported optimizer type '{opt_type}'")
            opt_param = dict(params)
            opt_param.pop("type", None)
            return opt_type, opt_param

        def cycle_iterator(iterable: Iterable) -> Generator[Any, None, None]:
            """
            Produces an infinite iterator by repeatedly cycling through the given iterable.

            Args:
                iterable (Iterable): The iterable to cycle through.

            Yields
            ------
            Any: The next item from the iterable, cycling back to the beginning when the end is reached.
            """
            while True:
                with torch.device("cpu"):
                    it = iter(iterable)
                yield from it

        def get_data_loader(
            _training_data: DpLoaderSet | LmdbDataset,
            _validation_data: DpLoaderSet | LmdbDataset | None,
            _training_params: dict[str, Any],
        ) -> tuple[
            DataLoader,
            Generator[Any, None, None],
            DataLoader | None,
            Generator[Any, None, None] | None,
            int,
        ]:
            def get_dataloader_and_iter_lmdb(
                _data: LmdbDataset,
            ) -> tuple[DataLoader, Generator[Any, None, None]]:
                if _data.mixed_batch:
                    # TODO [mixed_batch=True]: Replace SameNlocBatchSampler with
                    # RandomSampler(replacement=False) + padding collate_fn.
                    # Changes needed:
                    #   1. _collate_lmdb_batch: pad coord/force/atype to max_nloc,
                    #      add "atom_mask" bool tensor (nframes, max_nloc)
                    #   2. Use RandomSampler(_data, replacement=False) as sampler
                    #   3. Use fixed batch_size in DataLoader (not batch_sampler)
                    #   4. Model forward: apply atom_mask to descriptor/fitting
                    #   5. Loss: mask out padded atoms in force loss
                    raise NotImplementedError(
                        "mixed_batch=True training is not yet supported."
                    )
                # mixed_batch=False: group frames by nloc, each batch same nloc.
                # SameNlocBatchSampler yields list[int] per batch, all same nloc.
                # Auto batch_size is computed per-nloc-group inside the sampler.
                from deepmd.dpmodel.utils.lmdb_data import (
                    SameNlocBatchSampler,
                )

                _block_targets = getattr(_data, "_block_targets", None)

                if self.world_size > 1:
                    from deepmd.dpmodel.utils.lmdb_data import (
                        DistributedSameNlocBatchSampler,
                    )

                    _inner_sampler = DistributedSameNlocBatchSampler(
                        _data._reader,
                        rank=self.rank,
                        world_size=self.world_size,
                        shuffle=True,
                        seed=_training_params.get("seed", None),
                        block_targets=_block_targets,
                    )
                else:
                    _inner_sampler = SameNlocBatchSampler(
                        _data._reader,
                        shuffle=True,
                        block_targets=_block_targets,
                    )

                _batch_sampler = _SameNlocBatchSamplerTorch(_inner_sampler)
                _dataloader = DataLoader(
                    _data,
                    batch_sampler=_batch_sampler,
                    num_workers=0,
                    collate_fn=_collate_lmdb_batch,
                    pin_memory=(DEVICE != "cpu"),
                )
                _data_iter = cycle_iterator(_dataloader)
                return _dataloader, _data_iter

            def get_dataloader_and_iter(
                _data: DpLoaderSet, _params: dict[str, Any]
            ) -> tuple[DataLoader, Generator[Any, None, None]]:
                _sampler = get_sampler_from_params(_data, _params)
                if _sampler is None:
                    log.warning(
                        "Sampler not specified!"
                    )  # None sampler will lead to a premature stop iteration. Replacement should be True in attribute of the sampler to produce expected number of items in one iteration.
                _dataloader = DataLoader(
                    _data,
                    sampler=_sampler,
                    batch_size=None,
                    num_workers=NUM_WORKERS
                    if dist.is_available()
                    else 0,  # setting to 0 diverges the behavior of its iterator; should be >=1
                    drop_last=False,
                    collate_fn=lambda batch: batch,  # prevent extra conversion
                    pin_memory=(DEVICE != "cpu"),  # pin memory only if not on CPU
                )
                _data_iter = cycle_iterator(_dataloader)
                return _dataloader, _data_iter

            if isinstance(_training_data, LmdbDataset):
                training_dataloader, training_data_iter = get_dataloader_and_iter_lmdb(
                    _training_data
                )
            else:
                training_dataloader, training_data_iter = get_dataloader_and_iter(
                    _training_data, _training_params["training_data"]
                )

            if _validation_data is not None:
                if isinstance(_validation_data, LmdbDataset):
                    (
                        validation_dataloader,
                        validation_data_iter,
                    ) = get_dataloader_and_iter_lmdb(_validation_data)
                else:
                    (
                        validation_dataloader,
                        validation_data_iter,
                    ) = get_dataloader_and_iter(
                        _validation_data, _training_params["validation_data"]
                    )
                valid_numb_batch = _training_params["validation_data"].get(
                    "numb_btch", 1
                )
            else:
                validation_dataloader = None
                validation_data_iter = None
                valid_numb_batch = 1
            return (
                training_dataloader,
                training_data_iter,
                validation_dataloader,
                validation_data_iter,
                valid_numb_batch,
            )

        def single_model_stat(
            _model: Any,
            _data_stat_nbatch: int,
            _training_data: DpLoaderSet,
            _stat_file_path: str | None,
            finetune_has_new_type: bool = False,
            preset_observed_type: list[str] | None = None,
        ) -> Callable[[], Any]:
            @functools.lru_cache
            def get_sample() -> Any:
                sampled = make_stat_input(
                    _training_data.systems,
                    _training_data.dataloaders,
                    _data_stat_nbatch,
                )
                return sampled

            if (not resuming or finetune_has_new_type) and self.rank == 0:
                _model.compute_or_load_stat(
                    sampled_func=get_sample,
                    stat_file_path=_stat_file_path,
                    preset_observed_type=preset_observed_type,
                )
                if isinstance(_stat_file_path, DPH5Path):
                    _stat_file_path.root.close()
            return get_sample

        def get_lr(lr_params: dict[str, Any]) -> BaseLR:
            lr_params["num_steps"] = self.num_steps
            lr_schedule = BaseLR(**lr_params)
            return lr_schedule

        # Optimizer
        self.opt_type, self.opt_param = get_opt_param(optimizer_params)
        if self.zero_stage > 0 and self.multi_task:
            raise ValueError(
                "training.zero_stage is currently only supported in single-task training."
            )
        if self.zero_stage > 0 and self.opt_type == "LKF":
            raise ValueError("training.zero_stage does not support LKF optimizer.")

        # loss_param_tmp for Hessian activation
        loss_param_tmp = None
        if not self.multi_task:
            loss_param_tmp = config["loss"]
        else:
            loss_param_tmp = {
                model_key: config["loss_dict"][model_key]
                for model_key in self.model_keys
            }

        # Model
        self.model = get_model_for_wrapper(
            model_params,
            resuming=resuming,
            _loss_params=loss_param_tmp,
        )

        # Loss
        if not self.multi_task:
            self.loss = get_loss(
                config["loss"],
                config["learning_rate"]["start_lr"],
                len(model_params["type_map"]),
                self.model,
            )
        else:
            self.loss = {}
            for model_key in self.model_keys:
                loss_param = config["loss_dict"][model_key]
                lr_param = config["learning_rate"]["start_lr"]
                ntypes = len(model_params["model_dict"][model_key]["type_map"])
                self.loss[model_key] = get_loss(
                    loss_param, lr_param, ntypes, self.model[model_key]
                )

        # Data
        if not self.multi_task:
            # add data requirement for labels
            data_requirement = self.loss.label_requirement
            data_requirement += get_additional_data_requirement(self.model)
            training_data.add_data_requirement(data_requirement)
            if validation_data is not None:
                validation_data.add_data_requirement(data_requirement)
            # Preload and apply modifiers to all data before computing statistics
            training_data.preload_and_modify_all_data_torch()
            if validation_data is not None:
                validation_data.preload_and_modify_all_data_torch()
            self.get_sample_func = single_model_stat(
                self.model,
                model_params.get("data_stat_nbatch", 10),
                training_data,
                stat_file_path,
                finetune_has_new_type=self.finetune_links["Default"].get_has_new_type()
                if self.finetune_links is not None
                else False,
                preset_observed_type=model_params.get("info", {}).get("observed_type"),
            )
            # Persist observed_type from stat into model_params and model_def_script
            if not resuming and self.rank == 0:
                observed = self.model.atomic_model.observed_type
                if observed is not None:
                    model_params.setdefault("info", {})["observed_type"] = observed
                    self.model.model_def_script = json.dumps(model_params)
            (
                self.training_dataloader,
                self.training_data,
                self.validation_dataloader,
                self.validation_data,
                self.valid_numb_batch,
            ) = get_data_loader(training_data, validation_data, training_params)
            training_data.print_summary(
                "training",
                to_numpy_array(self.training_dataloader.sampler.weights)
                if not isinstance(training_data, LmdbDataset)
                else [1.0],
            )
            if validation_data is not None:
                validation_data.print_summary(
                    "validation",
                    to_numpy_array(self.validation_dataloader.sampler.weights)
                    if not isinstance(validation_data, LmdbDataset)
                    else [1.0],
                )
        else:
            (
                self.training_dataloader,
                self.training_data,
                self.validation_dataloader,
                self.validation_data,
                self.valid_numb_batch,
                self.get_sample_func,
            ) = {}, {}, {}, {}, {}, {}
            for model_key in self.model_keys:
                # add data requirement for labels
                data_requirement = self.loss[model_key].label_requirement
                data_requirement += get_additional_data_requirement(
                    self.model[model_key]
                )
                training_data[model_key].add_data_requirement(data_requirement)
                if validation_data[model_key] is not None:
                    validation_data[model_key].add_data_requirement(data_requirement)
                # Preload and apply modifiers to all data before computing statistics
                training_data[model_key].preload_and_modify_all_data_torch()
                if validation_data[model_key] is not None:
                    validation_data[model_key].preload_and_modify_all_data_torch()
                _mt_user_observed = (
                    model_params["model_dict"][model_key]
                    .get("info", {})
                    .get("observed_type")
                )
                self.get_sample_func[model_key] = single_model_stat(
                    self.model[model_key],
                    model_params["model_dict"][model_key].get("data_stat_nbatch", 10),
                    training_data[model_key],
                    stat_file_path[model_key],
                    finetune_has_new_type=self.finetune_links[
                        model_key
                    ].get_has_new_type()
                    if self.finetune_links is not None
                    else False,
                    preset_observed_type=_mt_user_observed,
                )
                # Persist observed_type into model_params and model_def_script
                if not resuming and self.rank == 0:
                    observed = self.model[model_key].atomic_model.observed_type
                    if observed is not None:
                        model_params["model_dict"][model_key].setdefault("info", {})[
                            "observed_type"
                        ] = observed
                        self.model[model_key].model_def_script = json.dumps(
                            model_params["model_dict"][model_key]
                        )

                (
                    self.training_dataloader[model_key],
                    self.training_data[model_key],
                    self.validation_dataloader[model_key],
                    self.validation_data[model_key],
                    self.valid_numb_batch[model_key],
                ) = get_data_loader(
                    training_data[model_key],
                    validation_data[model_key],
                    training_params["data_dict"][model_key],
                )

                training_data[model_key].print_summary(
                    f"training in {model_key}",
                    to_numpy_array(self.training_dataloader[model_key].sampler.weights)
                    if not isinstance(training_data[model_key], LmdbDataset)
                    else [1.0],
                )
                if (
                    validation_data is not None
                    and validation_data[model_key] is not None
                ):
                    validation_data[model_key].print_summary(
                        f"validation in {model_key}",
                        to_numpy_array(
                            self.validation_dataloader[model_key].sampler.weights
                        )
                        if not isinstance(validation_data[model_key], LmdbDataset)
                        else [1.0],
                    )

        # Resolve training steps
        per_task_total = []
        if not self.multi_task:
            if self.num_steps is None:
                if self.num_epoch is None:
                    raise ValueError(
                        "Either training.numb_steps or training.num_epoch must be set."
                    )
                if self.num_epoch <= 0:
                    raise ValueError("training.num_epoch must be positive.")
                if isinstance(training_data, LmdbDataset):
                    total_numb_batch = training_data.total_batch
                else:
                    sampler_weights = to_numpy_array(
                        self.training_dataloader.sampler.weights
                    )
                    total_numb_batch = compute_total_numb_batch(
                        training_data.index,
                        sampler_weights,
                    )
                if total_numb_batch <= 0:
                    raise ValueError(
                        "Total number of training batches must be positive."
                    )
                self.num_steps = int(np.ceil(self.num_epoch * total_numb_batch))
                log.info(
                    "Computed num_steps=%d from num_epoch=%s and total_numb_batch=%d.",
                    self.num_steps,
                    self.num_epoch,
                    total_numb_batch,
                )
        else:
            if self.num_epoch_dict:
                if self.num_steps is not None:
                    raise ValueError(
                        "training.numb_steps and training.num_epoch_dict "
                        "are mutually exclusive."
                    )
                for model_key in self.model_keys:
                    if isinstance(training_data[model_key], LmdbDataset):
                        per_task_total.append(training_data[model_key].total_batch)
                    else:
                        sampler_weights = to_numpy_array(
                            self.training_dataloader[model_key].sampler.weights
                        )
                        per_task_total.append(
                            compute_total_numb_batch(
                                training_data[model_key].index,
                                sampler_weights,
                            )
                        )
                (
                    self.model_prob,
                    self.num_steps,
                    per_task_steps,
                ) = resolve_model_prob_from_epochs(
                    self.model_keys,
                    self.num_epoch_dict,
                    np.asarray(per_task_total, dtype=np.float64),
                )
                log.info(
                    "Computed model_prob=%s and num_steps=%d from num_epoch_dict=%s "
                    "with per-task target steps: %s.",
                    self.model_prob,
                    self.num_steps,
                    self.num_epoch_dict,
                    {k: int(np.ceil(v)) for k, v in per_task_steps.items()},
                )
            else:
                if self.num_steps is None:
                    raise ValueError(
                        "Either training.numb_steps (multi-task only) or "
                        "training.num_epoch_dict must be set."
                    )
                self.model_prob = resolve_model_prob(
                    self.model_keys,
                    training_params.get("model_prob"),
                    training_data,
                    rank=self.rank,
                )

        # Learning rate
        self.gradient_max_norm = training_params.get("gradient_max_norm", 0.0)
        self.lr_schedule = get_lr(config["learning_rate"])

        # JIT
        if JIT:
            self.model = torch.jit.script(self.model)

        # Model Wrapper
        self.wrapper = ModelWrapper(self.model, self.loss, model_params=model_params)
        self.start_step = 0

        # resuming and finetune
        optimizer_state_dict = None
        ema_state_dict = None
        if resuming:
            log.info(f"Resuming from {resume_model}.")
            state_dict = torch.load(
                resume_model, map_location=DEVICE, weights_only=True
            )
            if "model" in state_dict:
                ema_state_dict = (
                    state_dict.get(EMA_CHECKPOINT_KEY)
                    if finetune_model is None and self.restart_training
                    else None
                )
                optimizer_state_dict = (
                    state_dict["optimizer"] if finetune_model is None else None
                )
                state_dict = state_dict["model"]
            self.start_step = (
                state_dict["_extra_state"]["train_infos"]["step"]
                if self.restart_training
                else 0
            )
            if self.rank == 0:
                if force_load:
                    input_keys = list(state_dict.keys())
                    target_keys = list(self.wrapper.state_dict().keys())
                    missing_keys = [
                        item for item in target_keys if item not in input_keys
                    ]
                    if missing_keys:
                        target_state_dict = self.wrapper.state_dict()
                        slim_keys = []
                        for item in missing_keys:
                            state_dict[item] = target_state_dict[item].clone().detach()
                            new_key = True
                            for slim_key in slim_keys:
                                if slim_key in item:
                                    new_key = False
                                    break
                            if new_key:
                                tmp_keys = ".".join(item.split(".")[:3])
                                slim_keys.append(tmp_keys)
                        slim_keys = [i + ".*" for i in slim_keys]
                        log.warning(
                            f"Force load mode allowed! These keys are not in ckpt and will re-init: {slim_keys}"
                        )
                # update model params in the pretrained model
                if finetune_model is not None:
                    new_state_dict = {}
                    target_state_dict = self.wrapper.state_dict()
                    # pretrained_model
                    pretrained_model = get_model_for_wrapper(
                        state_dict["_extra_state"]["model_params"]
                    )
                    pretrained_model_wrapper = ModelWrapper(pretrained_model)
                    pretrained_model_wrapper.load_state_dict(state_dict)
                    # update type related params
                    for model_key in self.model_keys:
                        finetune_rule_single = self.finetune_links[model_key]
                        _model_key_from = finetune_rule_single.get_model_branch()
                        # skip if updated
                        if (
                            finetune_rule_single.get_finetune_tmap()
                            != pretrained_model_wrapper.model[
                                _model_key_from
                            ].get_type_map()
                        ):
                            model_with_new_type_stat = None
                            if finetune_rule_single.get_has_new_type():
                                self.finetune_update_stat = True
                                model_with_new_type_stat = self.wrapper.model[model_key]
                            pretrained_model_wrapper.model[
                                _model_key_from
                            ].change_type_map(
                                finetune_rule_single.get_finetune_tmap(),
                                model_with_new_type_stat=model_with_new_type_stat,
                            )
                    state_dict = pretrained_model_wrapper.state_dict()

                    def collect_single_finetune_params(
                        _model_key: str,
                        _finetune_rule_single: Any,
                        _new_state_dict: dict[str, Any],
                        _origin_state_dict: dict[str, Any],
                        _random_state_dict: dict[str, Any],
                    ) -> None:
                        _new_fitting = _finetune_rule_single.get_random_fitting()
                        _model_key_from = _finetune_rule_single.get_model_branch()
                        target_keys = [
                            i
                            for i in _random_state_dict.keys()
                            if i != "_extra_state" and f".{_model_key}." in i
                        ]
                        for item_key in target_keys:
                            new_key = item_key.replace(
                                f".{_model_key}.", f".{_model_key_from}."
                            )
                            use_random_initialization = _new_fitting and (
                                ".descriptor." not in item_key
                            )
                            if (
                                not use_random_initialization
                                and new_key not in _origin_state_dict
                            ):
                                # for ZBL models finetuning from standard models
                                if ".models.0." in new_key:
                                    new_key = new_key.replace(".models.0.", ".")
                                elif ".models.1." in new_key:
                                    use_random_initialization = True
                                else:
                                    raise KeyError(
                                        f"Key {new_key} not found in pretrained model."
                                    )
                            if use_random_initialization:
                                # print(f'Keep {item_key} in old model!')
                                _new_state_dict[item_key] = (
                                    _random_state_dict[item_key].clone().detach()
                                )
                            else:
                                # print(f'Replace {item_key} with {new_key} in pretrained_model!')
                                _new_state_dict[item_key] = (
                                    _origin_state_dict[new_key].clone().detach()
                                )

                    # collect model params from the pretrained model
                    for model_key in self.model_keys:
                        finetune_rule_single = self.finetune_links[model_key]
                        collect_single_finetune_params(
                            model_key,
                            finetune_rule_single,
                            new_state_dict,
                            state_dict,
                            target_state_dict,
                        )
                    state_dict = new_state_dict
                    state_dict["_extra_state"] = self.wrapper.state_dict()[
                        "_extra_state"
                    ]

                self.wrapper.load_state_dict(state_dict)

                # change bias for fine-tuning
                if finetune_model is not None:

                    def single_model_finetune(
                        _model: Any,
                        _finetune_rule_single: Any,
                        _sample_func: Callable,
                    ) -> Any:
                        _model = model_change_out_bias(
                            _model,
                            _sample_func,
                            _bias_adjust_mode="change-by-statistic"
                            if not _finetune_rule_single.get_random_fitting()
                            else "set-by-statistic",
                        )
                        return _model

                    if not self.multi_task:
                        finetune_rule_single = self.finetune_links["Default"]
                        self.model = single_model_finetune(
                            self.model, finetune_rule_single, self.get_sample_func
                        )
                    else:
                        for model_key in self.model_keys:
                            finetune_rule_single = self.finetune_links[model_key]
                            if not finetune_rule_single.get_resuming():
                                log.info(
                                    f"Model branch {model_key} will be fine-tuned. This may take a long time..."
                                )
                                self.model[model_key] = single_model_finetune(
                                    self.model[model_key],
                                    finetune_rule_single,
                                    self.get_sample_func[model_key],
                                )
                            else:
                                log.info(
                                    f"Model branch {model_key} will resume training."
                                )

        if init_frz_model is not None:
            frz_model = torch.jit.load(init_frz_model, map_location=DEVICE)
            state = frz_model.state_dict()
            missing, unexpected = self.model.load_state_dict(state, strict=False)
            if missing or unexpected:
                log.warning(
                    f"Checkpoint loaded non-strictly. Missing keys: {missing}, Unexpected keys: {unexpected}"
                )

        # Multi-task share params
        if shared_links is not None:
            _data_stat_protect = np.array(
                [
                    model_params["model_dict"][ii].get("data_stat_protect", 1e-2)
                    for ii in model_params["model_dict"]
                ]
            )
            assert np.allclose(_data_stat_protect, _data_stat_protect[0]), (
                "Model key 'data_stat_protect' must be the same in each branch when multitask!"
            )
            self.wrapper.share_params(
                shared_links,
                resume=(resuming and not self.finetune_update_stat) or self.rank != 0,
                model_key_prob_map=dict(zip(self.model_keys, self.model_prob)),
                data_stat_protect=_data_stat_protect[0],
            )

        if self.is_distributed:
            torch.cuda.set_device(LOCAL_RANK)
            if self.zero_stage >= 2:
                if fully_shard is None:
                    raise RuntimeError(
                        "training.zero_stage>=2 requires FSDP2, which is only "
                        "available in PyTorch >= 2.6 "
                        "(``torch.distributed.fsdp.fully_shard``). "
                        f"Current PyTorch is {torch.__version__}. "
                        "Please upgrade PyTorch, or set training.zero_stage "
                        "to 0 or 1 to stay on the DDP / ZeRO-1 path."
                    )
                # FSDP2 does NOT broadcast params (unlike DDP constructor).
                # Ensure all ranks share identical weights before sharding.
                for p in self.wrapper.parameters():
                    dist.broadcast(p.data, src=0)
                for b in self.wrapper.buffers():
                    dist.broadcast(b.data, src=0)
                reshard = self.zero_stage >= 3
                self.wrapper = fully_shard(self.wrapper, reshard_after_forward=reshard)
            else:
                # zero_stage=0 or 1: standard DDP (ZeRO-1 will wrap the optimizer)
                self.wrapper = DDP(
                    self.wrapper,
                    device_ids=[LOCAL_RANK],
                    find_unused_parameters=True,
                    output_device=LOCAL_RANK,
                )

        # TODO add lr warmups for multitask
        # author: iProzd
        # TODO add optimizers for multitask
        # author: iProzd
        initial_lr = self.lr_schedule.value(self.start_step)
        if self.opt_type == "LKF":
            self.optimizer = LKFOptimizer(
                self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"]
            )
        else:
            # === Common path for gradient-based optimizers ===
            adam_betas = (
                float(self.opt_param["adam_beta1"]),
                float(self.opt_param["adam_beta2"]),
            )
            weight_decay = float(self.opt_param["weight_decay"])

            if self.opt_type in ("Adam", "AdamW"):
                cls = torch.optim.Adam if self.opt_type == "Adam" else torch.optim.AdamW
                extra = {"betas": adam_betas, "fused": DEVICE.type != "cpu"}
            elif self.opt_type == "AdaMuon":
                cls = AdaMuonOptimizer
                extra = {
                    "adam_betas": adam_betas,
                    "momentum": float(self.opt_param["momentum"]),
                    "lr_adjust": float(self.opt_param["lr_adjust"]),
                    "lr_adjust_coeff": float(self.opt_param["lr_adjust_coeff"]),
                }
            elif self.opt_type == "HybridMuon":
                cls = HybridMuonOptimizer
                extra = {
                    "adam_betas": adam_betas,
                    "momentum": float(self.opt_param["momentum"]),
                    "lr_adjust": float(self.opt_param["lr_adjust"]),
                    "lr_adjust_coeff": float(self.opt_param["lr_adjust_coeff"]),
                    "muon_mode": str(self.opt_param.get("muon_mode", "slice")),
                    "named_parameters": tuple(self.wrapper.named_parameters()),
                    "enable_gram": bool(self.opt_param.get("enable_gram")),
                    "flash_muon": bool(self.opt_param.get("flash_muon")),
                    "magma_muon": bool(self.opt_param.get("magma_muon")),
                    # FSDP2 shards parameters as DTensor; several torch._foreach_*
                    # ops lack DTensor sharding propagation on older PyTorch, so
                    # fall back to the per-tensor path under zero_stage >= 2.
                    # DDP / ZeRO-1 keep plain tensors and use the default.
                    "use_foreach": False if self.zero_stage >= 2 else None,
                }
            else:
                raise ValueError(f"Not supported optimizer type '{self.opt_type}'")

            self.optimizer = self._create_optimizer(
                cls,
                lr=initial_lr,
                weight_decay=weight_decay,
                **extra,
            )
            self._load_optimizer_state(optimizer_state_dict)
            self.scheduler = self._create_lr_scheduler(
                self.optimizer,
                self.lr_schedule,
                self.start_step,
            )

        self.model_ema = None
        if self.enable_ema:
            self.model_ema = ModelEMA(
                self.model,
                decay=self.ema_decay,
                state=ema_state_dict,
            )

        if self.zero_stage > 0 and self.rank == 0:
            if self.zero_stage == 1:
                log.info("Enabled DDP + ZeRO Stage-1 Optimizer State Sharding.")
            else:
                stage = (
                    "FULL_SHARD (Stage 3)"
                    if self.zero_stage >= 3
                    else "SHARD_GRAD_OP (Stage 2)"
                )
                log.info(f"Enabled FSDP2 {stage}.")

        # Tensorboard
        self.enable_tensorboard = training_params.get("tensorboard", False)
        self.tensorboard_log_dir = training_params.get("tensorboard_log_dir", "log")
        self.tensorboard_freq = training_params.get("tensorboard_freq", 1)
        self.enable_profiler = training_params.get("enable_profiler", False)
        self.profiling = training_params.get("profiling", False)
        self.profiling_file = training_params.get("profiling_file", "timeline.json")
        self.full_validator = None
        self.ema_full_validator = None

        validating_params = config.get("validating") or {}
        self.full_validator = self._create_full_validator(
            validating_params=validating_params,
            validation_data=validation_data,
        )
        self.ema_full_validator = self._create_ema_full_validator(
            validating_params=validating_params,
            validation_data=validation_data,
        )

        # Log model parameter count
        if self.rank == 0:
            self._log_parameter_count()

    @staticmethod
    def _create_lr_scheduler(
        optimizer: torch.optim.Optimizer,
        lr_schedule: BaseLR,
        start_step: int,
    ) -> torch.optim.lr_scheduler.LambdaLR:
        base_lr = float(lr_schedule.start_lr)
        for group in optimizer.param_groups:
            group["initial_lr"] = base_lr
        return torch.optim.lr_scheduler.LambdaLR(
            optimizer,
            lambda step: lr_schedule.value(step) / base_lr,
            last_epoch=start_step - 1,
        )

    def _create_full_validator(
        self,
        *,
        validating_params: dict[str, Any],
        validation_data: DpLoaderSet | None,
    ) -> FullValidator | None:
        """Create the runtime full validator when it is active."""
        if not self._is_validation_requested(validating_params, "full_validation"):
            return None
        self._raise_if_full_validation_unsupported(validation_data)
        if validation_data is None:
            raise RuntimeError(
                "validation_data must be available after full validation checks."
            )
        return FullValidator(
            validating_params=validating_params,
            validation_data=validation_data,
            model=self.model,
            state_store=self._get_inner_module().train_infos,
            num_steps=self.num_steps,
            rank=self.rank,
            zero_stage=self.zero_stage,
            restart_training=self.restart_training,
            checkpoint_dir=Path(self.save_ckpt).parent,
        )

    def _create_ema_full_validator(
        self,
        *,
        validating_params: dict[str, Any],
        validation_data: DpLoaderSet | None,
    ) -> FullValidator | None:
        """Create the runtime EMA full validator when it is active."""
        if not self._is_validation_requested(
            validating_params, "full_validation"
        ) or not validating_params.get("ema_full_validation", False):
            return None
        self._raise_if_full_validation_unsupported(validation_data)
        if self.model_ema is None:
            raise ValueError(
                "validating.ema_full_validation requires `training.enable_ema=true`."
            )
        if validation_data is None:
            raise RuntimeError(
                "validation_data must be available after EMA full validation checks."
            )
        ema_validating_params = dict(validating_params)
        ema_validating_params["full_validation"] = True
        return FullValidator(
            validating_params=ema_validating_params,
            validation_data=validation_data,
            model=self.model,
            state_store=self.model_ema.validation_state,
            num_steps=self.num_steps,
            rank=self.rank,
            zero_stage=self.zero_stage,
            restart_training=self.restart_training,
            checkpoint_dir=Path(self.save_ckpt).parent,
            full_val_file=get_ema_validation_log_path(
                validating_params.get("full_val_file", "val.log")
            ),
            best_checkpoint_prefix="best_ema.ckpt",
            emit_best_save_log=False,
            model_eval_context=lambda: self.model_ema.apply_shadow(self.model),
        )

    def _is_validation_requested(
        self,
        validating_params: dict[str, Any],
        flag_name: str,
    ) -> bool:
        """Check whether a full validation flow can trigger during this run."""
        if not validating_params.get(flag_name, False):
            return False
        start_step = resolve_full_validation_start_step(
            validating_params.get("full_val_start", 0.5),
            self.num_steps,
        )
        return start_step is not None and start_step <= self.num_steps

    def _raise_if_full_validation_unsupported(
        self,
        validation_data: DpLoaderSet | None,
    ) -> None:
        """Validate runtime full validation constraints."""
        if self.multi_task:
            raise ValueError(
                "validating.full_validation only supports single-task energy "
                "training; multi-task training is not supported."
            )

        has_spin = getattr(self.model, "has_spin", False)
        if callable(has_spin):
            has_spin = has_spin()
        if has_spin or isinstance(self.loss, EnergySpinLoss):
            raise ValueError(
                "validating.full_validation only supports single-task energy "
                "training; spin-energy training is not supported."
            )

        if not isinstance(self.loss, EnergyStdLoss):
            raise ValueError(
                "validating.full_validation only supports single-task energy training."
            )

        if validation_data is None:
            raise ValueError(
                "validating.full_validation requires `training.validation_data` "
                "to be configured."
            )

        if self.zero_stage >= 2:
            raise ValueError(
                "validating.full_validation only supports single-task energy "
                "training with training.zero_stage < 2."
            )

    @staticmethod
    def _count_parameters(model: torch.nn.Module) -> tuple[int, int]:
        """
        Count model parameters.

        Parameters
        ----------
        model : torch.nn.Module
            The model to count parameters for.

        Returns
        -------
        tuple[int, int]
            A tuple of (trainable, total) parameter counts.
        """
        trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
        total = sum(p.numel() for p in model.parameters())
        return trainable, total

    def _log_parameter_count(self) -> None:
        """Log model parameter count."""
        if not self.multi_task:
            trainable, total = self._count_parameters(self.model)
            log.info(
                f"Model Params:  {total / 1e6:.3f} M   (Trainable: {trainable / 1e6:.3f} M)"
            )
        else:
            log.warning(
                "In multitask mode, parameters may be shared across tasks. "
                "The following per-task counts may include duplicates."
            )
            for model_key in self.model_keys:
                trainable, total = self._count_parameters(self.model[model_key])
                log.info(
                    f"Model Params [{model_key}]: {total / 1e6:.3f} M   (Trainable: {trainable / 1e6:.3f} M)"
                )

    def _create_optimizer(
        self,
        optimizer_class: type[torch.optim.Optimizer],
        **kwargs: Any,
    ) -> torch.optim.Optimizer:
        """
        Construct optimizer, wrapping with ZeroRedundancyOptimizer when zero_stage=1.

        Parameters
        ----------
        optimizer_class : type[torch.optim.Optimizer]
            The optimizer class to instantiate.
        **kwargs : Any
            Keyword arguments forwarded to the optimizer constructor.

        Returns
        -------
        torch.optim.Optimizer
            Constructed optimizer instance.
        """
        if self.zero_stage == 1:
            return ZeroRedundancyOptimizer(
                self.wrapper.parameters(),
                optimizer_class=optimizer_class,
                **kwargs,
            )
        return optimizer_class(self.wrapper.parameters(), **kwargs)

    def _get_inner_module(self) -> ModelWrapper:
        """Unwrap DDP if needed. FSDP2 is in-place so no unwrapping required."""
        if self.is_distributed and self.zero_stage <= 1:
            return self.wrapper.module
        return self.wrapper

    def _load_optimizer_state(
        self, optimizer_state_dict: dict[str, Any] | None
    ) -> None:
        """Load optimizer state for restart training when available."""
        if optimizer_state_dict is None or not self.restart_training:
            return
        if self.zero_stage >= 2:
            set_optimizer_state_dict(
                self.wrapper,
                self.optimizer,
                optim_state_dict=optimizer_state_dict,
                options=StateDictOptions(
                    full_state_dict=True, broadcast_from_rank0=True
                ),
            )
        else:
            self.optimizer.load_state_dict(optimizer_state_dict)

    def run(self) -> None:
        fout = (
            open(
                self.disp_file,
                mode="w" if not self.restart_training else "a",
                buffering=1,
            )
            if self.rank == 0
            else None
        )  # line buffered
        if SAMPLER_RECORD:
            record_file = f"Sample_rank_{self.rank}.txt"
            fout1 = open(record_file, mode="w", buffering=1)
        log.info("Start to train %d steps.", self.num_steps)
        if dist.is_available() and dist.is_initialized():
            log.info(f"Rank: {dist.get_rank()}/{dist.get_world_size()}")
        if self.enable_tensorboard:
            from torch.utils.tensorboard import (
                SummaryWriter,
            )

            writer = SummaryWriter(log_dir=self.tensorboard_log_dir)
        if self.enable_profiler or self.profiling:
            prof = torch.profiler.profile(
                schedule=torch.profiler.schedule(wait=1, warmup=15, active=3, repeat=1),
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    self.tensorboard_log_dir
                )
                if self.enable_profiler
                else None,
                record_shapes=True,
                with_stack=True,
            )
            prof.start()

        def step(_step_id: int, task_key: str = "Default") -> None:
            display_step_id = _step_id + 1
            if self.multi_task:
                model_index = dp_random.choice(
                    np.arange(self.num_model, dtype=np.int_),
                    p=self.model_prob,
                )
                task_key = self.model_keys[model_index]
            # PyTorch Profiler
            if self.enable_profiler or self.profiling:
                prof.step()
            cur_lr = self.lr_schedule.value(_step_id)
            pref_lr = cur_lr
            self.optimizer.zero_grad(set_to_none=True)
            input_dict, label_dict, log_dict = self.get_data(
                is_train=True, task_key=task_key
            )
            if SAMPLER_RECORD:
                print_str = f"Step {_step_id}: sample system{log_dict['sid']}  frame{log_dict['fid']}\n"
                fout1.write(print_str)
                fout1.flush()
            if self.opt_type in ["Adam", "AdamW", "AdaMuon", "HybridMuon"]:
                cur_lr = self.scheduler.get_last_lr()[0]
                pref_lr = cur_lr
                model_pred, loss, more_loss = self.wrapper(
                    **input_dict, cur_lr=pref_lr, label=label_dict, task_key=task_key
                )
                loss.backward()
                # === Initialize gradient diagnostics variables ===
                total_norm: torch.Tensor | None = None
                pre_clip_named_norms: list[tuple[str, float]] = []
                if self.gradient_max_norm > 0.0:
                    # Collect per-parameter gradient norms before clipping.
                    # NOTE: Under FSDP2 with ZeRO stage >= 2, p.grad is a sharded DTensor,
                    # so p.grad.norm() computes the shard-local L2 norm, not the full-parameter
                    # norm. Skip per-param collection in this case to avoid misleading values.
                    if (
                        self.enable_tensorboard
                        and self.zero_stage < 2
                        and (
                            display_step_id % self.tensorboard_freq == 0
                            or display_step_id == 1
                        )
                    ):
                        pre_clip_named_norms = [
                            (name, p.grad.detach().norm().item())
                            for name, p in self.wrapper.named_parameters()
                            if p.grad is not None
                        ]
                    # FSDP2 sharded DTensor gradients don't support error_if_nonfinite; use manual isfinite check instead.
                    total_norm = torch.nn.utils.clip_grad_norm_(
                        self.wrapper.parameters(),
                        self.gradient_max_norm,
                    )
                    if not torch.isfinite(total_norm):
                        bad_params = []
                        for name, p in self.wrapper.named_parameters():
                            if p.grad is not None:
                                grad_norm = p.grad.data.norm()
                                if not torch.isfinite(grad_norm):
                                    bad_params.append(
                                        f"  {name}: grad_norm={grad_norm}, shape={list(p.shape)}"
                                    )
                        detail = (
                            "\n".join(bad_params)
                            if bad_params
                            else "  (all individual grads finite, overflow in norm reduction)"
                        )
                        raise RuntimeError(
                            f"Non-finite gradient norm: {total_norm}\n"
                            f"Parameters with non-finite gradients:\n{detail}"
                        )
                with torch.device(DEVICE):
                    self.optimizer.step()
                self.scheduler.step()
            elif self.opt_type == "LKF":
                if isinstance(self.loss, EnergyStdLoss):
                    KFOptWrapper = KFOptimizerWrapper(
                        self.wrapper,
                        self.optimizer,
                        24,
                        6,
                        dist.is_available() and dist.is_initialized(),
                    )
                    pref_e = self.opt_param["kf_start_pref_e"] * (
                        self.opt_param["kf_limit_pref_e"]
                        / self.opt_param["kf_start_pref_e"]
                    ) ** (_step_id / self.num_steps)
                    _ = KFOptWrapper.update_energy(
                        input_dict, label_dict["energy"], pref_e
                    )
                    pref_f = self.opt_param["kf_start_pref_f"] * (
                        self.opt_param["kf_limit_pref_f"]
                        / self.opt_param["kf_start_pref_f"]
                    ) ** (_step_id / self.num_steps)
                    p_energy, p_force = KFOptWrapper.update_force(
                        input_dict, label_dict["force"], pref_f
                    )
                    # [coord, atype, natoms, mapping, shift, nlist, box]
                    model_pred = {"energy": p_energy, "force": p_force}
                    module = (
                        self.wrapper.module
                        if dist.is_available() and dist.is_initialized()
                        else self.wrapper
                    )

                    def fake_model() -> dict:
                        return model_pred

                    _, loss, more_loss = module.loss[task_key](
                        {},
                        fake_model,
                        label_dict,
                        int(input_dict["atype"].shape[-1]),
                        learning_rate=pref_lr,
                    )
                elif isinstance(self.loss, DenoiseLoss):
                    KFOptWrapper = KFOptimizerWrapper(
                        self.wrapper,
                        self.optimizer,
                        24,
                        6,
                        dist.is_available() and dist.is_initialized(),
                    )
                    module = (
                        self.wrapper.module
                        if dist.is_available() and dist.is_initialized()
                        else self.wrapper
                    )
                    model_pred = KFOptWrapper.update_denoise_coord(
                        input_dict,
                        label_dict["clean_coord"],
                        1,
                        module.loss[task_key].mask_loss_coord,
                        label_dict["coord_mask"],
                    )
                    loss, more_loss = module.loss[task_key](
                        model_pred,
                        label_dict,
                        input_dict["natoms"],
                        learning_rate=pref_lr,
                    )
            else:
                raise ValueError(f"Not supported optimizer type '{self.opt_type}'")

            if self.model_ema is not None:
                self.model_ema.update(self.model)

            if self.disp_avg:
                # Accumulate loss for averaging over display interval
                self.step_count_in_interval += 1
                if not self.multi_task:
                    # Accumulate loss for single task
                    if not self.train_loss_accu:
                        # Initialize accumulator with current loss structure
                        for item in more_loss:
                            if "l2_" not in item:
                                self.train_loss_accu[item] = 0.0
                    for item in more_loss:
                        if "l2_" not in item:
                            self.train_loss_accu[item] += more_loss[item]
                else:
                    # Accumulate loss for multi-task
                    if task_key not in self.train_loss_accu:
                        self.train_loss_accu[task_key] = {}
                    if task_key not in self.step_count_per_task:
                        self.step_count_per_task[task_key] = 0
                    self.step_count_per_task[task_key] += 1

                    for item in more_loss:
                        if "l2_" not in item:
                            if item not in self.train_loss_accu[task_key]:
                                self.train_loss_accu[task_key][item] = 0.0
                            self.train_loss_accu[task_key][item] += more_loss[item]

            # Log and persist
            if self.display_in_training and (
                display_step_id % self.disp_freq == 0 or display_step_id == 1
            ):
                self.wrapper.eval()  # Will set to train mode before fininshing validation

                if self.disp_avg:

                    def log_loss_train(
                        _loss: Any, _more_loss: Any, _task_key: str = "Default"
                    ) -> dict:
                        results = {}
                        if not self.multi_task:
                            # Use accumulated average loss for single task
                            for item in self.train_loss_accu:
                                results[item] = (
                                    self.train_loss_accu[item]
                                    / self.step_count_in_interval
                                )
                        else:
                            # Use accumulated average loss for multi-task
                            if (
                                _task_key in self.train_loss_accu
                                and _task_key in self.step_count_per_task
                            ):
                                for item in self.train_loss_accu[_task_key]:
                                    results[item] = (
                                        self.train_loss_accu[_task_key][item]
                                        / self.step_count_per_task[_task_key]
                                    )
                        return results
                else:

                    def log_loss_train(
                        _loss: Any, _more_loss: Any, _task_key: str = "Default"
                    ) -> dict:
                        results = {}
                        rmse_val = {
                            item: _more_loss[item]
                            for item in _more_loss
                            if "l2_" not in item
                        }
                        for item in sorted(rmse_val.keys()):
                            results[item] = rmse_val[item]
                        return results

                def log_loss_valid(_task_key: str = "Default") -> dict:
                    single_results = {}
                    sum_natoms = 0
                    if not self.multi_task:
                        valid_numb_batch = self.valid_numb_batch
                    else:
                        valid_numb_batch = self.valid_numb_batch[_task_key]
                    for ii in range(valid_numb_batch):
                        self.optimizer.zero_grad()
                        input_dict, label_dict, _ = self.get_data(
                            is_train=False, task_key=_task_key
                        )
                        if input_dict == {}:
                            # no validation data
                            return {}
                        _, loss, more_loss = self.wrapper(
                            **input_dict,
                            cur_lr=pref_lr,
                            label=label_dict,
                            task_key=_task_key,
                        )
                        # more_loss.update({"rmse": math.sqrt(loss)})
                        natoms = int(input_dict["atype"].shape[-1])
                        sum_natoms += natoms
                        for k, v in more_loss.items():
                            if "l2_" not in k:
                                single_results[k] = (
                                    single_results.get(k, 0.0) + v * natoms
                                )
                    results = {k: v / sum_natoms for k, v in single_results.items()}
                    return results

                if not self.multi_task:
                    train_results = log_loss_train(loss, more_loss)
                    valid_results = log_loss_valid()
                    if self.rank == 0:
                        log.info(
                            format_training_message_per_task(
                                batch=display_step_id,
                                task_name="trn",
                                rmse=train_results,
                                learning_rate=cur_lr,
                            )
                        )
                        if valid_results:
                            log.info(
                                format_training_message_per_task(
                                    batch=display_step_id,
                                    task_name="val",
                                    rmse=valid_results,
                                    learning_rate=None,
                                )
                            )
                else:
                    train_results = {_key: {} for _key in self.model_keys}
                    valid_results = {_key: {} for _key in self.model_keys}
                    if self.disp_avg:
                        # For multi-task, use accumulated average loss for all tasks
                        for _key in self.model_keys:
                            train_results[_key] = log_loss_train(
                                loss, more_loss, _task_key=_key
                            )
                    else:
                        train_results[task_key] = log_loss_train(
                            loss, more_loss, _task_key=task_key
                        )
                        for _key in self.model_keys:
                            if _key != task_key:
                                self.optimizer.zero_grad()
                                input_dict, label_dict, _ = self.get_data(
                                    is_train=True, task_key=_key
                                )
                                _, loss, more_loss = self.wrapper(
                                    **input_dict,
                                    cur_lr=pref_lr,
                                    label=label_dict,
                                    task_key=_key,
                                )
                                train_results[_key] = log_loss_train(
                                    loss, more_loss, _task_key=_key
                                )
                            valid_results[_key] = log_loss_valid(_task_key=_key)
                            if self.rank == 0:
                                log.info(
                                    format_training_message_per_task(
                                        batch=display_step_id,
                                        task_name=_key + "_trn",
                                        rmse=train_results[_key],
                                        learning_rate=cur_lr,
                                    )
                                )
                                if valid_results[_key]:
                                    log.info(
                                        format_training_message_per_task(
                                            batch=display_step_id,
                                            task_name=_key + "_val",
                                            rmse=valid_results[_key],
                                            learning_rate=None,
                                        )
                                    )
                self.wrapper.train()

                if self.disp_avg:
                    # Reset loss accumulators after display
                    if not self.multi_task:
                        for item in self.train_loss_accu:
                            self.train_loss_accu[item] = 0.0
                    else:
                        for task_key in self.model_keys:
                            if task_key in self.train_loss_accu:
                                for item in self.train_loss_accu[task_key]:
                                    self.train_loss_accu[task_key][item] = 0.0
                            if task_key in self.step_count_per_task:
                                self.step_count_per_task[task_key] = 0
                    self.step_count_in_interval = 0
                    self.last_display_step = display_step_id

                current_time = time.time()
                train_time = current_time - self.t0
                self.t0 = current_time
                if self.rank == 0 and self.timing_in_training:
                    eta = int(
                        (self.num_steps - display_step_id)
                        / min(self.disp_freq, display_step_id - self.start_step)
                        * train_time
                    )
                    log.info(
                        format_training_message(
                            batch=display_step_id,
                            wall_time=train_time,
                            eta=eta,
                            current_time=datetime.datetime.fromtimestamp(
                                current_time,
                                tz=datetime.timezone.utc,
                            ).astimezone(),
                        )
                    )
                if (
                    (self.num_steps - self.start_step)
                    <= 2 * self.disp_freq  # not enough steps
                    or (_step_id - self.start_step)
                    >= self.disp_freq  # skip first disp_freq steps
                ):
                    self.total_train_time += train_time
                    if display_step_id == 1:
                        self.timed_steps += 1
                    else:
                        self.timed_steps += min(
                            self.disp_freq, _step_id - self.start_step
                        )

                if fout:
                    if self.lcurve_should_print_header:
                        self.print_header(fout, train_results, valid_results)
                        self.lcurve_should_print_header = False
                    self.print_on_training(
                        fout, display_step_id, cur_lr, train_results, valid_results
                    )

            if self.full_validator is not None:
                self.full_validator.run(
                    step_id=_step_id,
                    display_step=display_step_id,
                    lr=cur_lr,
                    save_checkpoint=self.save_model,
                )
            if self.ema_full_validator is not None:
                self.ema_full_validator.run(
                    step_id=_step_id,
                    display_step=display_step_id,
                    lr=cur_lr,
                    save_checkpoint=self.save_ema_model,
                )

            if (
                (
                    (display_step_id) % self.save_freq == 0
                    and _step_id != self.start_step
                )
                or (display_step_id) == self.num_steps
            ) and (self.zero_stage > 0 or self.rank == 0 or dist.get_rank() == 0):
                # Handle the case if rank 0 aborted and re-assigned
                self.latest_model = Path(self.save_ckpt + f"-{display_step_id}.pt")
                self.save_model(self.latest_model, lr=cur_lr, step=_step_id)
                if self.rank == 0 or dist.get_rank() == 0:
                    log.info(f"Saved model to {self.latest_model}")
                    symlink_prefix_files(self.latest_model.stem, self.save_ckpt)
                    with open("checkpoint", "w") as f:
                        f.write(str(self.latest_model))
                if self.model_ema is not None:
                    self.latest_ema_model = Path(
                        self.ema_save_ckpt + f"-{display_step_id}.pt"
                    )
                    self.save_ema_model(self.latest_ema_model, lr=cur_lr, step=_step_id)
                    if self.rank == 0 or dist.get_rank() == 0:
                        symlink_prefix_files(
                            self.latest_ema_model.stem,
                            self.ema_save_ckpt,
                        )

            # tensorboard
            if self.enable_tensorboard and (
                display_step_id % self.tensorboard_freq == 0 or display_step_id == 1
            ):
                writer.add_scalar(f"{task_key}/lr", cur_lr, display_step_id)
                writer.add_scalar(f"{task_key}/loss", loss, display_step_id)
                for item in more_loss:
                    writer.add_scalar(
                        f"{task_key}/{item}", more_loss[item], display_step_id
                    )
                # === Gradient diagnostics (pre-clip) ===
                # Only log if total_norm was computed (i.e., not LKF optimizer).
                if self.gradient_max_norm > 0.0 and total_norm is not None:
                    writer.add_scalar(
                        f"{task_key}/grad/total_norm",
                        total_norm.item(),
                        display_step_id,
                    )
                    # Only log per-parameter norms if list is non-empty.
                    if pre_clip_named_norms:
                        # Use float32 for histogram to ensure numerical stability
                        # when gradients are in lower precision (FP16/BF16).
                        norms = torch.tensor(
                            [gn for _, gn in pre_clip_named_norms],
                            dtype=torch.float32,
                            device="cpu",
                        )
                        writer.add_histogram(
                            f"{task_key}/grad/param_norms", norms, display_step_id
                        )
                        # Log top-10 largest per-parameter gradient norms.
                        pre_clip_named_norms.sort(key=lambda x: x[1], reverse=True)
                        for name, gn in pre_clip_named_norms[:10]:
                            writer.add_scalar(
                                f"{task_key}/grad_top10/{name}", gn, display_step_id
                            )

        self.wrapper.train()
        self.t0 = time.time()
        self.total_train_time = 0.0
        self.timed_steps = 0

        if self.disp_avg:
            # Initialize loss accumulators
            if not self.multi_task:
                self.train_loss_accu = {}
            else:
                self.train_loss_accu = {key: {} for key in self.model_keys}
                self.step_count_per_task = dict.fromkeys(self.model_keys, 0)
            self.step_count_in_interval = 0
            self.last_display_step = 0

        for step_id in range(self.start_step, self.num_steps):
            step(step_id)
            if JIT:
                break

        if (
            self.change_bias_after_training
            and self.num_steps > self.start_step
            and (self.rank == 0 or dist.get_rank() == 0)
        ):
            if not self.multi_task:
                self.model = model_change_out_bias(
                    self.model,
                    self.get_sample_func,
                    _bias_adjust_mode="change-by-statistic",
                )
            else:
                for model_key in self.model_keys:
                    self.model[model_key] = model_change_out_bias(
                        self.model[model_key],
                        self.get_sample_func[model_key],
                        _bias_adjust_mode="change-by-statistic",
                    )
            self.latest_model = Path(self.save_ckpt + f"-{self.num_steps}.pt")
            cur_lr = self.lr_schedule.value(self.num_steps - 1)
            self.save_model(self.latest_model, lr=cur_lr, step=self.num_steps - 1)
            log.info(f"Saved model to {self.latest_model}")
            symlink_prefix_files(self.latest_model.stem, self.save_ckpt)
            with open("checkpoint", "w") as f:
                f.write(str(self.latest_model))
            if self.model_ema is not None:
                self.latest_ema_model = Path(
                    self.ema_save_ckpt + f"-{self.num_steps}.pt"
                )
                self.save_ema_model(
                    self.latest_ema_model,
                    lr=cur_lr,
                    step=self.num_steps - 1,
                )
                symlink_prefix_files(self.latest_ema_model.stem, self.ema_save_ckpt)

        if self.num_steps == 0 and self.zero_stage > 0:
            # ZeRO-1 / FSDP: all ranks participate in save_model (collective op)
            self.latest_model = Path(self.save_ckpt + "-0.pt")
            self.save_model(self.latest_model, lr=0, step=0)
            if self.model_ema is not None:
                self.latest_ema_model = Path(self.ema_save_ckpt + "-0.pt")
                self.save_ema_model(self.latest_ema_model, lr=0, step=0)

        if (
            self.rank == 0 or dist.get_rank() == 0
        ):  # Handle the case if rank 0 aborted and re-assigned
            if self.num_steps == 0:
                if self.zero_stage == 0:
                    # When num_steps is 0, the checkpoint is never saved in the loop
                    self.latest_model = Path(self.save_ckpt + "-0.pt")
                    self.save_model(self.latest_model, lr=0, step=0)
                    if self.model_ema is not None:
                        self.latest_ema_model = Path(self.ema_save_ckpt + "-0.pt")
                        self.save_ema_model(self.latest_ema_model, lr=0, step=0)
                log.info(f"Saved model to {self.latest_model}")
                symlink_prefix_files(self.latest_model.stem, self.save_ckpt)
                with open("checkpoint", "w") as f:
                    f.write(str(self.latest_model))
                if self.model_ema is not None:
                    symlink_prefix_files(self.latest_ema_model.stem, self.ema_save_ckpt)

            if self.timing_in_training and self.timed_steps:
                msg = f"average training time: {self.total_train_time / self.timed_steps:.4f} s/batch"
                excluded_steps = self.num_steps - self.start_step - self.timed_steps
                if excluded_steps > 0:
                    msg += f" ({excluded_steps} batches excluded)"
                log.info(msg)

            if JIT:
                pth_model_path = (
                    "frozen_model.pth"  # We use .pth to denote the frozen model
                )
                self.model.save(pth_model_path)
                log.info(
                    f"Frozen model for inferencing has been saved to {pth_model_path}"
                )
            log.info(f"Trained model has been saved to: {self.save_ckpt}")

        if fout:
            fout.close()
        if SAMPLER_RECORD:
            fout1.close()
        if self.enable_tensorboard:
            writer.close()
        if self.enable_profiler or self.profiling:
            prof.stop()
            if self.enable_profiler:
                log.info(
                    f"The profiling trace has been saved under {self.tensorboard_log_dir}"
                )
            if not self.enable_profiler and self.profiling:
                prof.export_chrome_trace(self.profiling_file)
                log.info(
                    f"The profiling trace has been saved to: {self.profiling_file}"
                )

    def _collect_checkpoint_states(
        self,
        *,
        use_ema_weights: bool = False,
        include_optimizer: bool = True,
    ) -> tuple[dict[str, Any], dict[str, Any] | None]:
        """Collect model and optimizer states for checkpointing.

        Parameters
        ----------
        use_ema_weights : bool
            If True, temporarily swap in EMA shadow weights before collecting
            the model state dict.
        include_optimizer : bool
            If False, skip collecting the optimizer state. Used for EMA ckpts
            where the optimizer state is meaningless: EMA has no optimizer of
            its own, and the main model's optimizer state corresponds to a
            different parameter trajectory.

        Returns
        -------
        tuple[dict[str, Any], dict[str, Any] | None]
            (model_state, optim_state). optim_state is None when
            include_optimizer is False.
        """
        module = self._get_inner_module()
        ema_context = (
            self.model_ema.apply_shadow(self.model)
            if use_ema_weights and self.model_ema is not None
            else nullcontext()
        )
        with ema_context:
            if self.zero_stage >= 2:
                # FSDP2: collective op, all ranks participate; rank 0 gets full state
                options = StateDictOptions(full_state_dict=True, cpu_offload=True)
                model_state = get_model_state_dict(self.wrapper, options=options)
                optim_state = (
                    get_optimizer_state_dict(
                        self.wrapper, self.optimizer, options=options
                    )
                    if include_optimizer
                    else None
                )
            elif self.zero_stage == 1:
                # ZeRO-1: consolidate sharded optimizer state to rank 0.
                model_state = module.state_dict()
                if use_ema_weights:
                    # state_dict() tensors share storage with live parameters; clone
                    # them before the EMA context restores the original weights.
                    model_state = deepcopy(model_state)
                if include_optimizer:
                    self.optimizer.consolidate_state_dict(to=0)
                    optim_state = self.optimizer.state_dict() if self.rank == 0 else {}
                else:
                    optim_state = None
            else:
                model_state = module.state_dict()
                if use_ema_weights:
                    # Same storage-sharing issue as zero_stage == 1.
                    model_state = deepcopy(model_state)
                optim_state = self.optimizer.state_dict() if include_optimizer else None
        return model_state, optim_state

    @staticmethod
    def _parse_checkpoint_step(path: Path, prefix_name: str) -> int | None:
        """Parse the checkpoint step from ``<prefix>-<step>.pt`` filenames."""
        checkpoint_prefix = f"{prefix_name}-"
        if path.suffix != ".pt" or not path.name.startswith(checkpoint_prefix):
            return None
        step_text = path.name[len(checkpoint_prefix) : -len(path.suffix)]
        if not step_text.isdigit():
            return None
        return int(step_text)

    def _write_checkpoint(
        self,
        save_path: Path,
        checkpoint_data: dict[str, Any],
        *,
        ckpt_prefix: str,
        max_ckpt_keep: int,
    ) -> None:
        """Write a checkpoint file and apply prefix-based cleanup."""
        prefix_name = Path(ckpt_prefix).name

        # === Only rank 0 writes to disk ===
        if self.rank != 0:
            return
        optim_state = checkpoint_data.get("optimizer")
        if optim_state is not None:
            for item in optim_state["param_groups"]:
                item["lr"] = float(item["lr"])
        torch.save(checkpoint_data, save_path)
        checkpoint_dir = save_path.parent
        checkpoint_files = []
        for checkpoint_file in checkpoint_dir.glob("*.pt"):
            step = self._parse_checkpoint_step(checkpoint_file, prefix_name)
            if checkpoint_file.is_symlink() or step is None:
                continue
            checkpoint_files.append((checkpoint_file, step))

        current_step = self._parse_checkpoint_step(save_path, prefix_name)
        if current_step is not None:
            fresh_checkpoint_files = []
            for checkpoint_file, step in checkpoint_files:
                if step > current_step:
                    checkpoint_file.unlink()
                else:
                    fresh_checkpoint_files.append((checkpoint_file, step))
            checkpoint_files = fresh_checkpoint_files

        checkpoint_files.sort(key=lambda item: (item[1], item[0].name))
        while len(checkpoint_files) > max_ckpt_keep:
            checkpoint_files.pop(0)[0].unlink()

    def save_model(
        self,
        save_path: str | Path,
        lr: float = 0.0,
        step: int = 0,
        *,
        ckpt_prefix: str | None = None,
        max_ckpt_keep: int | None = None,
        use_ema_weights: bool = False,
        include_ema_state: bool = True,
        include_optimizer: bool = True,
    ) -> None:
        module = self._get_inner_module()
        module.train_infos["lr"] = float(lr)
        module.train_infos["step"] = step
        model_state, optim_state = self._collect_checkpoint_states(
            use_ema_weights=use_ema_weights,
            include_optimizer=include_optimizer,
        )
        checkpoint_data: dict[str, Any] = {"model": model_state}
        if optim_state is not None:
            checkpoint_data["optimizer"] = optim_state
        if include_ema_state and self.model_ema is not None and self.rank == 0:
            checkpoint_data[EMA_CHECKPOINT_KEY] = self.model_ema.state_dict()
        self._write_checkpoint(
            Path(save_path),
            checkpoint_data,
            ckpt_prefix=self.save_ckpt if ckpt_prefix is None else ckpt_prefix,
            max_ckpt_keep=(
                self.max_ckpt_keep if max_ckpt_keep is None else max_ckpt_keep
            ),
        )

    def save_ema_model(
        self, save_path: str | Path, lr: float = 0.0, step: int = 0
    ) -> None:
        """Save an EMA-weight checkpoint using the regular checkpoint format."""
        if self.model_ema is None:
            raise ValueError(
                "EMA checkpoint saving requires `training.enable_ema=true`."
            )
        self.save_model(
            save_path,
            lr=lr,
            step=step,
            ckpt_prefix=self.ema_save_ckpt,
            max_ckpt_keep=self.ema_ckpt_keep,
            use_ema_weights=True,
            include_ema_state=False,
            include_optimizer=False,
        )

    def get_data(
        self, is_train: bool = True, task_key: str = "Default"
    ) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
        if is_train:
            iterator = self.training_data
        else:
            iterator = self.validation_data
        if self.multi_task:
            iterator = iterator[task_key]
        if iterator is None:
            return {}, {}, {}
        batch_data = next(iterator)
        for key in batch_data.keys():
            if key == "sid" or key == "fid" or key == "box" or "find_" in key:
                continue
            elif not isinstance(batch_data[key], list):
                if batch_data[key] is not None:
                    batch_data[key] = batch_data[key].to(DEVICE, non_blocking=True)
            else:
                batch_data[key] = [
                    item.to(DEVICE, non_blocking=True) for item in batch_data[key]
                ]
        # we may need a better way to classify which are inputs and which are labels
        # now wrapper only supports the following inputs:
        input_keys = [
            "coord",
            "atype",
            "spin",
            "box",
            "fparam",
            "aparam",
            "charge_spin",
        ]
        input_dict = dict.fromkeys(input_keys)
        label_dict = {}
        for item_key in batch_data:
            if item_key in input_keys:
                if item_key == "fparam" and batch_data.get("find_fparam", 1.0) == 0.0:
                    continue
                if (
                    item_key == "charge_spin"
                    and batch_data.get("find_charge_spin", 1.0) == 0.0
                ):
                    continue
                input_dict[item_key] = batch_data[item_key]
            else:
                if item_key not in ["sid", "fid"]:
                    label_dict[item_key] = batch_data[item_key]
        log_dict = {}
        if "fid" in batch_data:
            log_dict["fid"] = batch_data["fid"]
        log_dict["sid"] = batch_data["sid"]
        return input_dict, label_dict, log_dict

    def print_header(
        self, fout: Any, train_results: dict[str, Any], valid_results: dict[str, Any]
    ) -> None:
        train_keys = sorted(train_results.keys())
        print_str = ""
        print_str += "# {:5s}".format("step")
        if not self.multi_task:
            if valid_results:
                prop_fmt = "   %11s %11s"
                for k in train_keys:
                    print_str += prop_fmt % (k + "_val", k + "_trn")
            else:
                prop_fmt = "   %11s"
                for k in train_keys:
                    print_str += prop_fmt % (k + "_trn")
        else:
            for model_key in self.model_keys:
                if valid_results[model_key]:
                    prop_fmt = "   %11s %11s"
                    for k in sorted(train_results[model_key].keys()):
                        print_str += prop_fmt % (
                            k + f"_val_{model_key}",
                            k + f"_trn_{model_key}",
                        )
                else:
                    prop_fmt = "   %11s"
                    for k in sorted(train_results[model_key].keys()):
                        print_str += prop_fmt % (k + f"_trn_{model_key}")
        print_str += "   {:8s}\n".format("lr")
        print_str += "# If there is no available reference data, rmse_*_{val,trn} will print nan\n"
        fout.write(print_str)
        fout.flush()

    def print_on_training(
        self,
        fout: Any,
        step_id: int,
        cur_lr: float,
        train_results: dict,
        valid_results: dict,
    ) -> None:
        train_keys = sorted(train_results.keys())
        print_str = ""
        print_str += f"{step_id:7d}"
        if not self.multi_task:
            if valid_results:
                prop_fmt = "   %11.2e %11.2e"
                for k in train_keys:
                    print_str += prop_fmt % (valid_results[k], train_results[k])
            else:
                prop_fmt = "   %11.2e"
                for k in train_keys:
                    print_str += prop_fmt % (train_results[k])
        else:
            for model_key in self.model_keys:
                if valid_results[model_key]:
                    prop_fmt = "   %11.2e %11.2e"
                    for k in sorted(valid_results[model_key].keys()):
                        print_str += prop_fmt % (
                            valid_results[model_key][k],
                            train_results[model_key][k],
                        )
                else:
                    prop_fmt = "   %11.2e"
                    for k in sorted(train_results[model_key].keys()):
                        print_str += prop_fmt % (train_results[model_key][k])
        print_str += f"   {cur_lr:8.1e}\n"
        fout.write(print_str)
        fout.flush()


def get_additional_data_requirement(_model: Any) -> list[DataRequirementItem]:
    additional_data_requirement = []
    if _model.get_dim_fparam() > 0:
        _fparam_default = (
            _model.get_default_fparam().cpu().numpy()
            if _model.has_default_fparam()
            else 0.0
        )
        fparam_requirement_items = [
            DataRequirementItem(
                "fparam",
                _model.get_dim_fparam(),
                atomic=False,
                must=not _model.has_default_fparam(),
                default=_fparam_default,
            )
        ]
        additional_data_requirement += fparam_requirement_items
    if _model.get_dim_aparam() > 0:
        aparam_requirement_items = [
            DataRequirementItem(
                "aparam", _model.get_dim_aparam(), atomic=True, must=True
            )
        ]
        additional_data_requirement += aparam_requirement_items
    has_spin = getattr(_model, "has_spin", False)
    if callable(has_spin):
        has_spin = has_spin()
    if has_spin:
        spin_requirement_items = [
            DataRequirementItem("spin", ndof=3, atomic=True, must=True)
        ]
        additional_data_requirement += spin_requirement_items
    if _model.has_chg_spin_ebd():
        has_default_cs = _model.has_default_chg_spin()
        cs_default = (
            _model.get_default_chg_spin().cpu().numpy() if has_default_cs else 0.0
        )
        additional_data_requirement.append(
            DataRequirementItem(
                "charge_spin",
                ndof=2,
                atomic=False,
                must=not has_default_cs,
                default=cs_default,
            )
        )
    return additional_data_requirement


def whether_hessian(loss_params: dict[str, Any]) -> bool:
    loss_type = loss_params.get("type", "ener")
    return loss_type == "ener" and loss_params.get("start_pref_h", 0.0) > 0.0


def get_loss(
    loss_params: dict[str, Any], start_lr: float, _ntypes: int, _model: Any
) -> TaskLoss:
    loss_type = loss_params.get("type", "ener")
    if whether_hessian(loss_params):
        loss_params["starter_learning_rate"] = start_lr
        return EnergyHessianStdLoss(**loss_params)
    elif loss_type == "ener":
        loss_params["starter_learning_rate"] = start_lr
        return EnergyStdLoss(**loss_params)
    elif loss_type == "dos":
        loss_params["starter_learning_rate"] = start_lr
        loss_params["numb_dos"] = _model.model_output_def()["dos"].output_size
        return DOSLoss(**loss_params)
    elif loss_type == "ener_spin":
        loss_params["starter_learning_rate"] = start_lr
        return EnergySpinLoss(**loss_params)
    elif loss_type == "denoise":
        loss_params["ntypes"] = _ntypes
        return DenoiseLoss(**loss_params)
    elif loss_type == "tensor":
        model_output_type = _model.model_output_type()
        if "mask" in model_output_type:
            model_output_type.pop(model_output_type.index("mask"))
        tensor_name = model_output_type[0]
        loss_params["tensor_size"] = _model.model_output_def()[tensor_name].output_size
        loss_params["label_name"] = tensor_name
        if tensor_name == "polarizability":
            tensor_name = "polar"
        loss_params["tensor_name"] = tensor_name
        return TensorLoss(**loss_params)
    elif loss_type == "property":
        task_dim = _model.get_task_dim()
        var_name = _model.get_var_name()
        intensive = _model.get_intensive()
        loss_params["task_dim"] = task_dim
        loss_params["var_name"] = var_name
        loss_params["intensive"] = intensive
        return PropertyLoss(**loss_params)
    else:
        loss_params["starter_learning_rate"] = start_lr
        return TaskLoss.get_class_by_type(loss_type).get_loss(loss_params)


def get_single_model(
    _model_params: dict[str, Any],
) -> Any:
    if "use_srtab" in _model_params:
        model = get_zbl_model(deepcopy(_model_params)).to(DEVICE)
    else:
        model = get_model(deepcopy(_model_params)).to(DEVICE)
    return model


def get_model_for_wrapper(
    _model_params: dict[str, Any],
    resuming: bool = False,
    _loss_params: dict[str, Any] | None = None,
) -> Any:
    if "model_dict" not in _model_params:
        if _loss_params is not None and whether_hessian(_loss_params):
            _model_params["hessian_mode"] = True
        _model = get_single_model(
            _model_params,
        )
    else:
        _model = {}
        model_keys = list(_model_params["model_dict"])
        do_case_embd, case_embd_index = get_case_embd_config(_model_params)
        for _model_key in model_keys:
            if _loss_params is not None and whether_hessian(_loss_params[_model_key]):
                _model_params["model_dict"][_model_key]["hessian_mode"] = True
            _model[_model_key] = get_single_model(
                _model_params["model_dict"][_model_key],
            )
            if do_case_embd and not resuming:
                # only set case_embd when from scratch multitask training
                _model[_model_key].set_case_embd(case_embd_index[_model_key])
    return _model


def get_case_embd_config(_model_params: dict[str, Any]) -> tuple[bool, dict[str, int]]:
    assert "model_dict" in _model_params, (
        "Only support setting case embedding for multi-task model!"
    )
    model_keys = list(_model_params["model_dict"])
    sorted_model_keys = sorted(model_keys)
    numb_case_embd_list = [
        _model_params["model_dict"][model_key]
        .get("fitting_net", {})
        .get("dim_case_embd", 0)
        for model_key in sorted_model_keys
    ]
    if not all(item == numb_case_embd_list[0] for item in numb_case_embd_list):
        raise ValueError(
            f"All models must have the same dimension of case embedding, while the settings are: {numb_case_embd_list}"
        )
    if numb_case_embd_list[0] == 0:
        return False, {}
    case_embd_index = {
        model_key: idx for idx, model_key in enumerate(sorted_model_keys)
    }
    return True, case_embd_index


def model_change_out_bias(
    _model: Any,
    _sample_func: Callable[[], Any],
    _bias_adjust_mode: str = "change-by-statistic",
) -> Any:
    old_bias = deepcopy(_model.get_out_bias())
    _model.change_out_bias(
        _sample_func,
        bias_adjust_mode=_bias_adjust_mode,
    )
    new_bias = deepcopy(_model.get_out_bias())

    from deepmd.pt.model.model.dp_model import (
        DPModelCommon,
    )

    if isinstance(_model, DPModelCommon) and _bias_adjust_mode == "set-by-statistic":
        _model.get_fitting_net().compute_input_stats(_sample_func)

    model_type_map = _model.get_type_map()
    log.info(
        f"Change output bias of {model_type_map!s} "
        f"from {to_numpy_array(old_bias).reshape(-1)[: len(model_type_map)]!s} "
        f"to {to_numpy_array(new_bias).reshape(-1)[: len(model_type_map)]!s}."
    )
    return _model