src/llmtuner/hparams/generating_args.py

from dataclasses import asdict, dataclass, field
from typing import Any, Dict, Optional


@dataclass
class GeneratingArguments:
    r"""
    Arguments pertaining to specify the decoding parameters.
    """
    do_sample: Optional[bool] = field(
        default=True, metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."}
    )
    temperature: Optional[float] = field(
        default=0.95, metadata={"help": "The value used to modulate the next token probabilities."}
    )
    top_p: Optional[float] = field(
        default=0.7,
        metadata={
            "help": "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
        },
    )
    top_k: Optional[int] = field(
        default=50,
        metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
    )
    num_beams: Optional[int] = field(
        default=1, metadata={"help": "Number of beams for beam search. 1 means no beam search."}
    )
    max_length: Optional[int] = field(
        default=512,
        metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
    )
    max_new_tokens: Optional[int] = field(
        default=512,
        metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
    )
    repetition_penalty: Optional[float] = field(
        default=1.0, metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."}
    )
    length_penalty: Optional[float] = field(
        default=1.0, metadata={"help": "Exponential penalty to the length that is used with beam-based generation."}
    )

    def to_dict(self) -> Dict[str, Any]:
        args = asdict(self)
        if args.get("max_new_tokens", -1) > 0:
            args.pop("max_length", None)
        else:
            args.pop("max_new_tokens", None)
        return args