Source code for lmflow.pipeline.auto_pipeline

#!/usr/bin/env python
"""Return a pipeline automatically based on its name."""

from lmflow.pipeline.evaluator import Evaluator
from lmflow.pipeline.finetuner import Finetuner
from lmflow.pipeline.inferencer import Inferencer
from lmflow.pipeline.rm_inferencer import RewardModelInferencer
from lmflow.pipeline.rm_tuner import RewardModelTuner
from lmflow.utils.versioning import is_package_version_at_least, is_ray_available, is_trl_available, is_vllm_available



PIPELINE_MAPPING = {
    "evaluator": Evaluator,
    "finetuner": Finetuner,
    "inferencer": Inferencer,
    "rm_inferencer": RewardModelInferencer,
    "rm_tuner": RewardModelTuner,
}



PIPELINE_NEEDS_EXTRAS = []


if not is_package_version_at_least("transformers", "4.35.0"):
    from lmflow.pipeline.raft_aligner import RaftAligner

    PIPELINE_MAPPING["raft_aligner"] = RaftAligner
else:
    PIPELINE_NEEDS_EXTRAS.append("raft_aligner")

if is_vllm_available():
    from lmflow.pipeline.vllm_inferencer import VLLMInferencer

    PIPELINE_MAPPING["vllm_inferencer"] = VLLMInferencer
else:
    PIPELINE_NEEDS_EXTRAS.append("vllm_inferencer")

if is_trl_available():
    from lmflow.pipeline.dpo_aligner import DPOAligner
    from lmflow.pipeline.dpov2_aligner import DPOv2Aligner

    PIPELINE_MAPPING["dpo_aligner"] = DPOAligner
    PIPELINE_MAPPING["dpov2_aligner"] = DPOv2Aligner
else:
    PIPELINE_NEEDS_EXTRAS.extend(["dpo_aligner", "dpov2_aligner"])

if is_vllm_available() and is_trl_available() and is_ray_available():
    from lmflow.pipeline.iterative_dpo_aligner import IterativeDPOAligner

    PIPELINE_MAPPING["iterative_dpo_aligner"] = IterativeDPOAligner
else:
    PIPELINE_NEEDS_EXTRAS.append("iterative_dpo_aligner")




class AutoPipeline:
    """
    The class designed to return a pipeline automatically based on its name.
    """

    @classmethod


    def get_pipeline(self, pipeline_name, model_args, data_args, pipeline_args, *args, **kwargs):
        if pipeline_name not in PIPELINE_MAPPING:
            if pipeline_name in PIPELINE_NEEDS_EXTRAS:
                raise NotImplementedError(
                    f'Please install the necessary dependencies to use pipeline "{pipeline_name}"'
                )

            raise NotImplementedError(f'Pipeline "{pipeline_name}" is not supported')

        pipeline = PIPELINE_MAPPING[pipeline_name](model_args, data_args, pipeline_args, *args, **kwargs)
        return pipeline