image-match/python/py/Lib/site-packages/ray/train/lightgbm/lightgbm_trainer.py

import logging
from functools import partial
from typing import Any, Callable, Dict, Optional, Union

import lightgbm

import ray
from ray.train import Checkpoint
from ray.train.constants import TRAIN_DATASET_KEY
from ray.train.lightgbm._lightgbm_utils import RayTrainReportCallback
from ray.train.lightgbm.config import LightGBMConfig
from ray.train.lightgbm.v2 import LightGBMTrainer as SimpleLightGBMTrainer
from ray.train.trainer import GenDataset
from ray.train.utils import _log_deprecation_warning
from ray.util.annotations import PublicAPI

logger = logging.getLogger(__name__)


LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE = (
    "Passing in `lightgbm.train` kwargs such as `params`, `num_boost_round`, "
    "`label_column`, etc. to `LightGBMTrainer` is deprecated "
    "in favor of the new API which accepts a `train_loop_per_worker` argument, "
    "similar to the other DataParallelTrainer APIs (ex: TorchTrainer). "
    "See this issue for more context: "
    "https://github.com/ray-project/ray/issues/50042"
)


def _lightgbm_train_fn_per_worker(
    config: dict,
    label_column: str,
    num_boost_round: int,
    dataset_keys: set,
    lightgbm_train_kwargs: dict,
):
    checkpoint = ray.train.get_checkpoint()
    starting_model = None
    remaining_iters = num_boost_round
    if checkpoint:
        starting_model = RayTrainReportCallback.get_model(checkpoint)
        starting_iter = starting_model.current_iteration()
        remaining_iters = num_boost_round - starting_iter
        logger.info(
            f"Model loaded from checkpoint will train for "
            f"additional {remaining_iters} iterations (trees) in order "
            "to achieve the target number of iterations "
            f"({num_boost_round=})."
        )

    train_ds_iter = ray.train.get_dataset_shard(TRAIN_DATASET_KEY)
    train_df = train_ds_iter.materialize().to_pandas()

    eval_ds_iters = {
        k: ray.train.get_dataset_shard(k)
        for k in dataset_keys
        if k != TRAIN_DATASET_KEY
    }
    eval_dfs = {k: d.materialize().to_pandas() for k, d in eval_ds_iters.items()}

    train_X, train_y = train_df.drop(label_column, axis=1), train_df[label_column]
    train_set = lightgbm.Dataset(train_X, label=train_y)

    # NOTE: Include the training dataset in the evaluation datasets.
    # This allows `train-*` metrics to be calculated and reported.
    valid_sets = [train_set]
    valid_names = [TRAIN_DATASET_KEY]

    for eval_name, eval_df in eval_dfs.items():
        eval_X, eval_y = eval_df.drop(label_column, axis=1), eval_df[label_column]
        valid_sets.append(lightgbm.Dataset(eval_X, label=eval_y))
        valid_names.append(eval_name)

    # Add network params of the worker group to enable distributed training.
    config.update(ray.train.lightgbm.get_network_params())
    config.setdefault("tree_learner", "data_parallel")
    config.setdefault("pre_partition", True)

    lightgbm.train(
        params=config,
        train_set=train_set,
        num_boost_round=remaining_iters,
        valid_sets=valid_sets,
        valid_names=valid_names,
        init_model=starting_model,
        **lightgbm_train_kwargs,
    )


@PublicAPI(stability="beta")
class LightGBMTrainer(SimpleLightGBMTrainer):
    """A Trainer for distributed data-parallel LightGBM training.

    Example
    -------

    .. testcode::
        :skipif: True

        import lightgbm

        import ray.data
        import ray.train
        from ray.train.lightgbm import RayTrainReportCallback, LightGBMTrainer

        def train_fn_per_worker(config: dict):
            # (Optional) Add logic to resume training state from a checkpoint.
            # ray.train.get_checkpoint()

            # 1. Get the dataset shard for the worker and convert to a `lightgbm.Dataset`
            train_ds_iter, eval_ds_iter = (
                ray.train.get_dataset_shard("train"),
                ray.train.get_dataset_shard("validation"),
            )
            train_ds, eval_ds = train_ds_iter.materialize(), eval_ds_iter.materialize()
            train_df, eval_df = train_ds.to_pandas(), eval_ds.to_pandas()
            train_X, train_y = train_df.drop("y", axis=1), train_df["y"]
            eval_X, eval_y = eval_df.drop("y", axis=1), eval_df["y"]
            dtrain = lightgbm.Dataset(train_X, label=train_y)
            deval = lightgbm.Dataset(eval_X, label=eval_y)

            params = {
                "objective": "regression",
                "metric": "l2",
                "learning_rate": 1e-4,
                "subsample": 0.5,
                "max_depth": 2,
                # Adding the line below is the only change needed
                # for your `lgb.train` call!
                **ray.train.lightgbm.get_network_params(),
            }

            # 2. Do distributed data-parallel training.
            # Ray Train sets up the necessary coordinator processes and
            # environment variables for your workers to communicate with each other.
            bst = lightgbm.train(
                params,
                train_set=dtrain,
                valid_sets=[deval],
                valid_names=["validation"],
                num_boost_round=10,
                callbacks=[RayTrainReportCallback()],
            )

        train_ds = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
        eval_ds = ray.data.from_items([{"x": x, "y": x + 1} for x in range(16)])
        trainer = LightGBMTrainer(
            train_fn_per_worker,
            datasets={"train": train_ds, "validation": eval_ds},
            scaling_config=ray.train.ScalingConfig(num_workers=4),
        )
        result = trainer.fit()
        booster = RayTrainReportCallback.get_model(result.checkpoint)

    Args:
        train_loop_per_worker: The training function to execute on each worker.
            This function can either take in zero arguments or a single ``Dict``
            argument which is set by defining ``train_loop_config``.
            Within this function you can use any of the
            :ref:`Ray Train Loop utilities <train-loop-api>`.
        train_loop_config: A configuration ``Dict`` to pass in as an argument to
            ``train_loop_per_worker``.
            This is typically used for specifying hyperparameters.
        lightgbm_config: The configuration for setting up the distributed lightgbm
            backend. Defaults to using the "rabit" backend.
            See :class:`~ray.train.lightgbm.LightGBMConfig` for more info.
        datasets: The Ray Datasets to use for training and validation.
        dataset_config: The configuration for ingesting the input ``datasets``.
            By default, all the Ray Datasets are split equally across workers.
            See :class:`~ray.train.DataConfig` for more details.
        scaling_config: The configuration for how to scale data parallel training.
            ``num_workers`` determines how many Python processes are used for training,
            and ``use_gpu`` determines whether or not each process should use GPUs.
            See :class:`~ray.train.ScalingConfig` for more info.
        run_config: The configuration for the execution of the training run.
            See :class:`~ray.train.RunConfig` for more info.
        resume_from_checkpoint: A checkpoint to resume training from.
            This checkpoint can be accessed from within ``train_loop_per_worker``
            by calling ``ray.train.get_checkpoint()``.
        metadata: Dict that should be made available via
            `ray.train.get_context().get_metadata()` and in `checkpoint.get_metadata()`
            for checkpoints saved from this Trainer. Must be JSON-serializable.
        label_column: [Deprecated] Name of the label column. A column with this name
            must be present in the training dataset.
        params: [Deprecated] LightGBM training parameters.
            Refer to `LightGBM documentation <https://lightgbm.readthedocs.io/>`_
            for a list of possible parameters.
        num_boost_round: [Deprecated] Target number of boosting iterations (trees in the model).
            Note that unlike in ``lightgbm.train``, this is the target number
            of trees, meaning that if you set ``num_boost_round=10`` and pass a model
            that has already been trained for 5 iterations, it will be trained for 5
            iterations more, instead of 10 more.
        **train_kwargs: [Deprecated] Additional kwargs passed to ``lightgbm.train()`` function.
    """

    _handles_checkpoint_freq = True
    _handles_checkpoint_at_end = True

    def __init__(
        self,
        train_loop_per_worker: Optional[
            Union[Callable[[], None], Callable[[Dict], None]]
        ] = None,
        *,
        train_loop_config: Optional[Dict] = None,
        lightgbm_config: Optional[LightGBMConfig] = None,
        scaling_config: Optional[ray.train.ScalingConfig] = None,
        run_config: Optional[ray.train.RunConfig] = None,
        datasets: Optional[Dict[str, GenDataset]] = None,
        dataset_config: Optional[ray.train.DataConfig] = None,
        resume_from_checkpoint: Optional[Checkpoint] = None,
        metadata: Optional[Dict[str, Any]] = None,
        # TODO: [Deprecated] Legacy LightGBMTrainer API
        label_column: Optional[str] = None,
        params: Optional[Dict[str, Any]] = None,
        num_boost_round: Optional[int] = None,
        **train_kwargs,
    ):
        # TODO: [Deprecated] Legacy LightGBMTrainer API
        legacy_api = train_loop_per_worker is None
        if legacy_api:
            train_loop_per_worker = self._get_legacy_train_fn_per_worker(
                lightgbm_train_kwargs=train_kwargs,
                run_config=run_config,
                label_column=label_column,
                num_boost_round=num_boost_round,
                datasets=datasets,
            )
            train_loop_config = params or {}
        elif train_kwargs:
            _log_deprecation_warning(
                "Passing `lightgbm.train` kwargs to `LightGBMTrainer` is deprecated. "
                f"Got kwargs: {train_kwargs.keys()}\n"
                "In your training function, you can call `lightgbm.train(**kwargs)` "
                "with arbitrary arguments. "
                f"{LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE}"
            )

        super(LightGBMTrainer, self).__init__(
            train_loop_per_worker=train_loop_per_worker,
            train_loop_config=train_loop_config,
            lightgbm_config=lightgbm_config,
            scaling_config=scaling_config,
            run_config=run_config,
            datasets=datasets,
            dataset_config=dataset_config,
            resume_from_checkpoint=resume_from_checkpoint,
            metadata=metadata,
        )

    def _get_legacy_train_fn_per_worker(
        self,
        lightgbm_train_kwargs: Dict,
        run_config: Optional[ray.train.RunConfig],
        datasets: Optional[Dict[str, GenDataset]],
        label_column: Optional[str],
        num_boost_round: Optional[int],
    ) -> Callable[[Dict], None]:
        """Get the training function for the legacy LightGBMTrainer API."""

        datasets = datasets or {}
        if not datasets.get(TRAIN_DATASET_KEY):
            raise ValueError(
                "`datasets` must be provided for the LightGBMTrainer API "
                "if `train_loop_per_worker` is not provided. "
                "This dict must contain the training dataset under the "
                f"key: '{TRAIN_DATASET_KEY}'. "
                f"Got keys: {list(datasets.keys())}"
            )
        if not label_column:
            raise ValueError(
                "`label_column` must be provided for the LightGBMTrainer API "
                "if `train_loop_per_worker` is not provided. "
                "This is the column name of the label in the dataset."
            )

        num_boost_round = num_boost_round or 10

        _log_deprecation_warning(LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE)

        # Initialize a default Ray Train metrics/checkpoint reporting callback if needed
        callbacks = lightgbm_train_kwargs.get("callbacks", [])
        user_supplied_callback = any(
            isinstance(callback, RayTrainReportCallback) for callback in callbacks
        )
        callback_kwargs = {}
        if run_config:
            checkpoint_frequency = run_config.checkpoint_config.checkpoint_frequency
            checkpoint_at_end = run_config.checkpoint_config.checkpoint_at_end

            callback_kwargs["frequency"] = checkpoint_frequency
            # Default `checkpoint_at_end=True` unless the user explicitly sets it.
            callback_kwargs["checkpoint_at_end"] = (
                checkpoint_at_end if checkpoint_at_end is not None else True
            )

        if not user_supplied_callback:
            callbacks.append(RayTrainReportCallback(**callback_kwargs))
        lightgbm_train_kwargs["callbacks"] = callbacks

        train_fn_per_worker = partial(
            _lightgbm_train_fn_per_worker,
            label_column=label_column,
            num_boost_round=num_boost_round,
            dataset_keys=set(datasets),
            lightgbm_train_kwargs=lightgbm_train_kwargs,
        )
        return train_fn_per_worker

    @classmethod
    def get_model(
        cls,
        checkpoint: Checkpoint,
    ) -> lightgbm.Booster:
        """Retrieve the LightGBM model stored in this checkpoint."""
        return RayTrainReportCallback.get_model(checkpoint)