chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import logging
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from functools import partial
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from typing import Any, Callable, Dict, Optional, Union
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import lightgbm
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import ray
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from ray.train import Checkpoint
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from ray.train.constants import TRAIN_DATASET_KEY
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from ray.train.lightgbm._lightgbm_utils import (
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RayTrainReportCallback,
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normalize_pandas_for_lightgbm,
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)
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from ray.train.lightgbm.config import LightGBMConfig
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from ray.train.lightgbm.v2 import LightGBMTrainer as SimpleLightGBMTrainer
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from ray.train.trainer import GenDataset
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from ray.train.utils import _log_deprecation_warning
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from ray.util.annotations import PublicAPI
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logger = logging.getLogger(__name__)
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LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE = (
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"Passing in `lightgbm.train` kwargs such as `params`, `num_boost_round`, "
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"`label_column`, etc. to `LightGBMTrainer` is deprecated "
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"in favor of the new API which accepts a `train_loop_per_worker` argument, "
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"similar to the other DataParallelTrainer APIs (ex: TorchTrainer). "
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"See this issue for more context: "
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"https://github.com/ray-project/ray/issues/50042"
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)
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def _lightgbm_train_fn_per_worker(
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config: dict,
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label_column: str,
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num_boost_round: int,
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dataset_keys: set,
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lightgbm_train_kwargs: dict,
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):
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checkpoint = ray.train.get_checkpoint()
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starting_model = None
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remaining_iters = num_boost_round
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if checkpoint:
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starting_model = RayTrainReportCallback.get_model(checkpoint)
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starting_iter = starting_model.current_iteration()
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remaining_iters = num_boost_round - starting_iter
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logger.info(
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f"Model loaded from checkpoint will train for "
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f"additional {remaining_iters} iterations (trees) in order "
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"to achieve the target number of iterations "
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f"({num_boost_round=})."
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)
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train_ds_iter = ray.train.get_dataset_shard(TRAIN_DATASET_KEY)
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train_df = normalize_pandas_for_lightgbm(train_ds_iter.materialize().to_pandas())
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eval_ds_iters = {
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k: ray.train.get_dataset_shard(k)
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for k in dataset_keys
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if k != TRAIN_DATASET_KEY
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}
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eval_dfs = {
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k: normalize_pandas_for_lightgbm(d.materialize().to_pandas())
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for k, d in eval_ds_iters.items()
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}
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train_X, train_y = train_df.drop(label_column, axis=1), train_df[label_column]
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train_set = lightgbm.Dataset(train_X, label=train_y)
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# NOTE: Include the training dataset in the evaluation datasets.
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# This allows `train-*` metrics to be calculated and reported.
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valid_sets = [train_set]
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valid_names = [TRAIN_DATASET_KEY]
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for eval_name, eval_df in eval_dfs.items():
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eval_X, eval_y = eval_df.drop(label_column, axis=1), eval_df[label_column]
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valid_sets.append(lightgbm.Dataset(eval_X, label=eval_y))
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valid_names.append(eval_name)
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# Add network params of the worker group to enable distributed training.
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config.update(ray.train.lightgbm.get_network_params())
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config.setdefault("tree_learner", "data_parallel")
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config.setdefault("pre_partition", True)
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lightgbm.train(
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params=config,
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train_set=train_set,
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num_boost_round=remaining_iters,
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valid_sets=valid_sets,
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valid_names=valid_names,
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init_model=starting_model,
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**lightgbm_train_kwargs,
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)
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@PublicAPI(stability="beta")
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class LightGBMTrainer(SimpleLightGBMTrainer):
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"""A Trainer for distributed data-parallel LightGBM training.
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Example:
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.. testcode::
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:skipif: True
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import lightgbm
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import ray.data
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import ray.train
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from ray.train.lightgbm import (
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LightGBMTrainer,
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RayTrainReportCallback,
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normalize_pandas_for_lightgbm,
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)
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def train_fn_per_worker(config: dict):
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# (Optional) Add logic to resume training state from a checkpoint.
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# ray.train.get_checkpoint()
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# 1. Get the dataset shard for the worker and convert to a `lightgbm.Dataset`
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train_ds_iter, eval_ds_iter = (
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ray.train.get_dataset_shard("train"),
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ray.train.get_dataset_shard("validation"),
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)
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train_ds, eval_ds = train_ds_iter.materialize(), eval_ds_iter.materialize()
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train_df = normalize_pandas_for_lightgbm(train_ds.to_pandas())
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eval_df = normalize_pandas_for_lightgbm(eval_ds.to_pandas())
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train_X, train_y = train_df.drop("y", axis=1), train_df["y"]
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eval_X, eval_y = eval_df.drop("y", axis=1), eval_df["y"]
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dtrain = lightgbm.Dataset(train_X, label=train_y)
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deval = lightgbm.Dataset(eval_X, label=eval_y)
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params = {
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"objective": "regression",
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"metric": "l2",
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"learning_rate": 1e-4,
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"subsample": 0.5,
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"max_depth": 2,
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# Adding the line below is the only change needed
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# for your `lgb.train` call!
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**ray.train.lightgbm.get_network_params(),
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}
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# 2. Do distributed data-parallel training.
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# Ray Train sets up the necessary coordinator processes and
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# environment variables for your workers to communicate with each other.
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bst = lightgbm.train(
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params,
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train_set=dtrain,
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valid_sets=[deval],
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valid_names=["validation"],
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num_boost_round=10,
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callbacks=[RayTrainReportCallback()],
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)
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train_ds = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
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eval_ds = ray.data.from_items([{"x": x, "y": x + 1} for x in range(16)])
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trainer = LightGBMTrainer(
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train_fn_per_worker,
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datasets={"train": train_ds, "validation": eval_ds},
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scaling_config=ray.train.ScalingConfig(num_workers=4),
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)
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result = trainer.fit()
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booster = RayTrainReportCallback.get_model(result.checkpoint)
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Args:
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train_loop_per_worker: The training function to execute on each worker.
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This function can either take in zero arguments or a single ``Dict``
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argument which is set by defining ``train_loop_config``.
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Within this function you can use any of the
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:ref:`Ray Train Loop utilities <train-loop-api>`.
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train_loop_config: A configuration ``Dict`` to pass in as an argument to
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``train_loop_per_worker``.
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This is typically used for specifying hyperparameters.
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lightgbm_config: The configuration for setting up the distributed lightgbm
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backend. Defaults to using the "rabit" backend.
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See :class:`~ray.train.lightgbm.LightGBMConfig` for more info.
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scaling_config: The configuration for how to scale data parallel training.
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``num_workers`` determines how many Python processes are used for training,
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and ``use_gpu`` determines whether or not each process should use GPUs.
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See :class:`~ray.train.ScalingConfig` for more info.
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run_config: The configuration for the execution of the training run.
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See :class:`~ray.train.RunConfig` for more info.
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datasets: The Ray Datasets to use for training and validation.
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dataset_config: The configuration for ingesting the input ``datasets``.
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By default, all the Ray Datasets are split equally across workers.
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See :class:`~ray.train.DataConfig` for more details.
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resume_from_checkpoint: A checkpoint to resume training from.
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This checkpoint can be accessed from within ``train_loop_per_worker``
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by calling ``ray.train.get_checkpoint()``.
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metadata: Dict that should be made available via
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`ray.train.get_context().get_metadata()` and in `checkpoint.get_metadata()`
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for checkpoints saved from this Trainer. Must be JSON-serializable.
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label_column: [Deprecated] Name of the label column. A column with this name
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must be present in the training dataset.
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params: [Deprecated] LightGBM training parameters.
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Refer to `LightGBM documentation <https://lightgbm.readthedocs.io/>`_
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for a list of possible parameters.
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num_boost_round: [Deprecated] Target number of boosting iterations (trees in the model).
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Note that unlike in ``lightgbm.train``, this is the target number
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of trees, meaning that if you set ``num_boost_round=10`` and pass a model
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that has already been trained for 5 iterations, it will be trained for 5
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iterations more, instead of 10 more.
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**train_kwargs: [Deprecated] Additional kwargs passed to ``lightgbm.train()`` function.
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"""
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_handles_checkpoint_freq = True
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_handles_checkpoint_at_end = True
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def __init__(
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self,
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train_loop_per_worker: Optional[
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Union[Callable[[], None], Callable[[Dict], None]]
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] = None,
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*,
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train_loop_config: Optional[Dict] = None,
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lightgbm_config: Optional[LightGBMConfig] = None,
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scaling_config: Optional[ray.train.ScalingConfig] = None,
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run_config: Optional[ray.train.RunConfig] = None,
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datasets: Optional[Dict[str, GenDataset]] = None,
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dataset_config: Optional[ray.train.DataConfig] = None,
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resume_from_checkpoint: Optional[Checkpoint] = None,
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metadata: Optional[Dict[str, Any]] = None,
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# TODO: [Deprecated] Legacy LightGBMTrainer API
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label_column: Optional[str] = None,
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params: Optional[Dict[str, Any]] = None,
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num_boost_round: Optional[int] = None,
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**train_kwargs,
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):
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# TODO: [Deprecated] Legacy LightGBMTrainer API
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legacy_api = train_loop_per_worker is None
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if legacy_api:
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train_loop_per_worker = self._get_legacy_train_fn_per_worker(
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lightgbm_train_kwargs=train_kwargs,
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run_config=run_config,
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label_column=label_column,
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num_boost_round=num_boost_round,
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datasets=datasets,
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)
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train_loop_config = params or {}
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elif train_kwargs:
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_log_deprecation_warning(
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"Passing `lightgbm.train` kwargs to `LightGBMTrainer` is deprecated. "
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f"Got kwargs: {train_kwargs.keys()}\n"
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"In your training function, you can call `lightgbm.train(**kwargs)` "
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"with arbitrary arguments. "
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f"{LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE}"
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)
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super(LightGBMTrainer, self).__init__(
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train_loop_per_worker=train_loop_per_worker,
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train_loop_config=train_loop_config,
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lightgbm_config=lightgbm_config,
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scaling_config=scaling_config,
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run_config=run_config,
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datasets=datasets,
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dataset_config=dataset_config,
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resume_from_checkpoint=resume_from_checkpoint,
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metadata=metadata,
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)
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def _get_legacy_train_fn_per_worker(
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self,
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lightgbm_train_kwargs: Dict,
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run_config: Optional[ray.train.RunConfig],
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datasets: Optional[Dict[str, GenDataset]],
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label_column: Optional[str],
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num_boost_round: Optional[int],
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) -> Callable[[Dict], None]:
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"""Get the training function for the legacy LightGBMTrainer API."""
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datasets = datasets or {}
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if not datasets.get(TRAIN_DATASET_KEY):
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raise ValueError(
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"`datasets` must be provided for the LightGBMTrainer API "
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"if `train_loop_per_worker` is not provided. "
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"This dict must contain the training dataset under the "
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f"key: '{TRAIN_DATASET_KEY}'. "
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f"Got keys: {list(datasets.keys())}"
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)
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if not label_column:
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raise ValueError(
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"`label_column` must be provided for the LightGBMTrainer API "
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"if `train_loop_per_worker` is not provided. "
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"This is the column name of the label in the dataset."
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)
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num_boost_round = num_boost_round or 10
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_log_deprecation_warning(LEGACY_LIGHTGBM_TRAINER_DEPRECATION_MESSAGE)
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# Initialize a default Ray Train metrics/checkpoint reporting callback if needed
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callbacks = lightgbm_train_kwargs.get("callbacks", [])
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user_supplied_callback = any(
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isinstance(callback, RayTrainReportCallback) for callback in callbacks
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)
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callback_kwargs = {}
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if run_config:
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checkpoint_frequency = run_config.checkpoint_config.checkpoint_frequency
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checkpoint_at_end = run_config.checkpoint_config.checkpoint_at_end
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callback_kwargs["frequency"] = checkpoint_frequency
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# Default `checkpoint_at_end=True` unless the user explicitly sets it.
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callback_kwargs["checkpoint_at_end"] = (
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checkpoint_at_end if checkpoint_at_end is not None else True
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)
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if not user_supplied_callback:
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callbacks.append(RayTrainReportCallback(**callback_kwargs))
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lightgbm_train_kwargs["callbacks"] = callbacks
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train_fn_per_worker = partial(
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_lightgbm_train_fn_per_worker,
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label_column=label_column,
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num_boost_round=num_boost_round,
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dataset_keys=set(datasets),
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lightgbm_train_kwargs=lightgbm_train_kwargs,
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)
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return train_fn_per_worker
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@classmethod
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def get_model(
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cls,
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checkpoint: Checkpoint,
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) -> lightgbm.Booster:
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"""Retrieve the LightGBM model stored in this checkpoint."""
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return RayTrainReportCallback.get_model(checkpoint)
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