swift/arguments/base_args/generation_args.py

# Copyright (c) ModelScope Contributors. All rights reserved.
from dataclasses import dataclass, field
from typing import List, Optional

from swift.infer_engine import RequestConfig
from swift.utils import get_logger

logger = get_logger()


@dataclass
class GenerationArguments:
    """A dataclass that holds arguments for text generation.

    Args:
        max_new_tokens (Optional[int]): The maximum number of new tokens to generate. Defaults to None (unlimited).
        temperature (Optional[float]): The sampling temperature. A higher temperature makes the output more random. To
            disable randomness, you can set this to 0 or `top_k` to 1. Defaults to None, which means loading from
            'generation_config.json'.
        top_k (Optional[int]): The number of highest probability vocabulary tokens to keep for top-k-filtering.
            Defaults to None (reads from 'generation_config.json').
        top_p (Optional[float]): The cumulative probability for nucleus sampling. Filters the vocabulary to the
            smallest set of tokens whose cumulative probability exceeds `top_p`. Defaults to None (reads from
            'generation_config.json').
        repetition_penalty (Optional[float]): The penalty applied to repeated tokens. A value of 1.0 means no penalty.
            Defaults to None (reads from 'generation_config.json').
        num_beams (Optional[int]): The number of beams to use for beam search. Defaults to 1.
        stream (bool): Whether to enable streaming output. Defaults to None, which is `True` for interactive mode and
            `False` for batch inference. Note: For ms-swift < 3.6, the default is `False`.
        stop_words (List[str]): A list of extra stop words, in addition to the end-of-sequence token. Note: The
            `eos_token` is removed from the output, while these stop words are preserved. Defaults to an empty list.
        logprobs (bool): Whether to output log probabilities of the generated tokens. Defaults to False.
        top_logprobs (Optional[int]): The number of top log probabilities to return for each token position. Requires
            `logprobs` to be True. Defaults to None.
        structured_outputs_regex (Optional[str]): A regular expression pattern for structured outputs (guided decoding).
            When set, the model's generation is constrained to match the specified regex pattern. This is useful for
            tasks requiring structured outputs like reasoning chains. Only effective when `infer_backend` is 'vllm'.
            Defaults to None.
    """

    # generation config
    max_new_tokens: Optional[int] = None  # Unlimited, constrained by max_model_len.
    # If it is None, use the parameters from generation_config.
    temperature: Optional[float] = None  # Set to 0, which means do_sample is False.
    top_k: Optional[int] = None
    top_p: Optional[float] = None
    repetition_penalty: Optional[float] = None
    num_beams: int = 1

    stream: Optional[bool] = None
    stop_words: List[str] = field(default_factory=list)
    logprobs: bool = False
    top_logprobs: Optional[int] = None
    # structured outputs (guided decoding), only effective for vllm backend
    structured_outputs_regex: Optional[str] = None

    def _init_stream(self):
        if self.stream is None:
            self.stream = False

    def get_request_config(self):
        if getattr(self, 'task_type') != 'causal_lm':
            return

        return RequestConfig(
            max_tokens=self.max_new_tokens,
            temperature=self.temperature,
            top_p=self.top_p,
            top_k=self.top_k,
            num_beams=self.num_beams,
            stop=self.stop_words,
            stream=self.stream,
            repetition_penalty=self.repetition_penalty,
            logprobs=self.logprobs,
            top_logprobs=self.top_logprobs,
            structured_outputs_regex=self.structured_outputs_regex)