ray-project--ray/release/train_tests/benchmark/config.py

import argparse
import enum
from typing import ClassVar

from pydantic import BaseModel, Field


class DataloaderType(enum.Enum):
    RAY_DATA = "ray_data"
    MOCK = "mock"
    TORCH = "torch"


class DataLoaderConfig(BaseModel):
    train_batch_size: int = 32
    limit_training_rows: int = 1000000  # Use -1 for unlimited

    validation_batch_size: int = 256
    limit_validation_rows: int = 50000  # Use -1 for unlimited


class TaskConfig(BaseModel):
    TASK_NAME: ClassVar[str] = "base"


class ImageClassificationConfig(TaskConfig):
    TASK_NAME: ClassVar[str] = "image_classification"

    class ImageFormat(enum.Enum):
        JPEG = "jpeg"
        PARQUET = "parquet"
        S3_URL = "s3_url"

    image_classification_local_dataset: bool = False
    image_classification_data_format: ImageFormat = ImageFormat.PARQUET
    # When True and data_format=PARQUET, read from the larger
    # IMAGENET_PARQUET_SPLIT_1T_S3_ROOT dataset instead of the default
    # parquet_split root. Used by the slow-consumer benchmarks to sustain
    # backpressure.
    image_classification_use_1t_dataset: bool = False


class RecsysConfig(TaskConfig):
    TASK_NAME: ClassVar[str] = "recsys"


class RayDataConfig(DataLoaderConfig):
    # NOTE: Optional[int] doesn't play well with argparse.
    local_buffer_shuffle_size: int = -1
    enable_operator_progress_bars: bool = True
    ray_data_prefetch_batches: int = 4
    ray_data_override_num_blocks: int = -1
    locality_with_output: bool = False
    actor_locality_enabled: bool = True
    enable_shard_locality: bool = True
    preserve_order: bool = False
    ray_data_pin_memory: bool = False


class TorchConfig(DataLoaderConfig):
    num_torch_workers: int = 8
    torch_dataloader_timeout_seconds: int = 300
    torch_pin_memory: bool = True
    torch_non_blocking: bool = True
    torch_prefetch_factor: int = -1


class BenchmarkConfig(BaseModel):
    # ScalingConfig
    num_workers: int = 1
    # Elastic scaling range. If both are set > 0, use (min_workers, max_workers).
    min_workers: int = 0
    max_workers: int = 0
    # Run CPU training where train workers request a `MOCK_GPU` resource instead.
    mock_gpu: bool = False

    # FailureConfig
    max_failures: int = 0

    task: str = "image_classification"
    task_config: TaskConfig = Field(
        default_factory=lambda: TaskConfig(),
    )

    # Data
    dataloader_type: DataloaderType = DataloaderType.RAY_DATA
    dataloader_config: DataLoaderConfig = Field(
        default_factory=lambda: DataLoaderConfig(),
    )

    # Training
    num_epochs: int = 1
    skip_train_step: bool = False
    # Simulates a slow training consumer by sleeping for this many seconds
    # after each training step. Used to benchmark dataloader behavior under
    # consumer back-pressure. 0 disables the sleep.
    train_step_sleep_s: float = 0.0
    # Maximum number of training batches per worker per epoch. When reached,
    # the epoch ends early regardless of dataset size. -1 disables the cap.
    # Used with slow-consumer benchmarks to bound wall-clock without
    # truncating the data source.
    max_train_batches: int = -1

    # Checkpointing
    checkpoint_every_n_steps: int = -1

    # Validation
    validate_every_n_steps: int = -1
    skip_validation_step: bool = False
    skip_validation_at_epoch_end: bool = False

    # Logging
    log_metrics_every_n_steps: int = 512


def _is_pydantic_model(field_type) -> bool:
    """Check if a type is a subclass of Pydantic's BaseModel."""
    return isinstance(field_type, type) and issubclass(field_type, BaseModel)


def _str_to_bool(value: str) -> bool:
    """Convert a string to a boolean value."""
    if value.lower() == "true":
        return True
    elif value.lower() == "false":
        return False
    raise argparse.ArgumentTypeError(f"'True' or 'False' expected, got '{value}'")


def _add_field_to_parser(parser: argparse.ArgumentParser, field: str, field_info):
    field_type = field_info.annotation
    if field_type is bool:
        parser.add_argument(f"--{field}", type=_str_to_bool, default=field_info.default)
    else:
        parser.add_argument(f"--{field}", type=field_type, default=field_info.default)


def cli_to_config(benchmark_config_cls=BenchmarkConfig) -> BenchmarkConfig:
    parser = argparse.ArgumentParser()

    nested_fields = []
    for field, field_info in benchmark_config_cls.model_fields.items():
        # Skip nested configs for now
        if _is_pydantic_model(field_info.annotation):
            nested_fields.append(field)
            continue

        _add_field_to_parser(parser, field, field_info)

    top_level_args, _ = parser.parse_known_args()

    # Handle nested configs that depend on top-level args
    nested_configs = {}
    for nested_field in nested_fields:
        nested_parser = argparse.ArgumentParser()
        nested_config_cls = benchmark_config_cls.model_fields[nested_field].annotation

        if nested_config_cls == DataLoaderConfig:
            if top_level_args.dataloader_type == DataloaderType.RAY_DATA:
                nested_config_cls = RayDataConfig
            elif top_level_args.dataloader_type == DataloaderType.TORCH:
                nested_config_cls = TorchConfig

        if nested_config_cls == TaskConfig:
            if top_level_args.task == ImageClassificationConfig.TASK_NAME:
                nested_config_cls = ImageClassificationConfig
            elif top_level_args.task == RecsysConfig.TASK_NAME:
                nested_config_cls = RecsysConfig

        for field, field_info in nested_config_cls.model_fields.items():
            _add_field_to_parser(nested_parser, field, field_info)

        args, _ = nested_parser.parse_known_args()
        nested_configs[nested_field] = nested_config_cls(**vars(args))

    return benchmark_config_cls(**vars(top_level_args), **nested_configs)


if __name__ == "__main__":
    config = cli_to_config()
    print(config)