config.py

from typing import Any, Literal

from pydantic import Field, field_serializer, field_validator
from transformers import AutoConfig, PretrainedConfig
from transformers.models.qwen3.modeling_qwen3 import (
    Qwen3Config,
)

from speculators import SpeculatorModelConfig

__all__ = [
    "DFlashSpeculatorConfig",
]


@SpeculatorModelConfig.register("dflash")
class DFlashSpeculatorConfig(SpeculatorModelConfig):
    """
    Configuration for DFlash speculator with vocabulary mapping.

    DFlash features vocabulary mapping between the 32K draft vocabulary and
    the 262,144-token target vocabulary, enabling reduced-vocabulary speculation.

    :param transformer_layer_config: Configuration for the transformer decoder layer
    :param draft_vocab_size: Size of draft model vocabulary for speculation
    """

    speculators_model_type: Literal["dflash"] = "dflash"
    architectures: list[str] = Field(
        default_factory=lambda: ["DFlashSpeculator"],
        description="Model architectures that can load these weights",
    )

    transformer_layer_config: PretrainedConfig = Field(
        default_factory=Qwen3Config,
        description="Configuration for the transformer decoder layer",
    )

    draft_vocab_size: int = Field(
        default=32000,
        description="Size of draft model vocabulary for speculation",
    )

    block_size: int = Field(
        default=8,
        description=(
            "Default size of the draft block predicted with a forward pass of the model"
        ),
    )

    target_hidden_size: int | None = Field(
        default=None,
        description="Hidden size of the target model (if different from draft model)",
    )

    aux_hidden_state_layer_ids: list[int] | None = Field(
        default=None,
        description="Layer IDs of the DFlash auxiliary hidden state layers",
    )

    mask_token_id: int | None = Field(
        default=None,
        description="Token ID used for masking",
    )

    sliding_window_non_causal: bool = Field(
        default=False,
        description="Use non-causal (bidirectional) masking within draft blocks for "
        "sliding window attention layers. Full attention layers are always "
        "bidirectional.",
    )

    sample_from_anchor: bool = Field(
        default=False,
        description=(
            "Whether to sample from the anchor position. "
            "False: anchor is the bonus token, only mask tokens predict "
            "(block_size-1 speculative tokens). "
            "True: sample from anchor and all mask positions "
            "(block_size speculative tokens). "
        ),
    )

    @field_serializer("transformer_layer_config")
    def serialize_transformer_config(self, value: PretrainedConfig) -> dict:
        """Serialize transformer config to dict."""
        return value.to_diff_dict()

    @field_validator("transformer_layer_config", mode="before")
    @classmethod
    def validate_transformer_config(cls, value: Any) -> PretrainedConfig:
        """Validate and convert transformer config."""
        if isinstance(value, dict):
            config_class: type[PretrainedConfig] = Qwen3Config
            if "model_type" in value:
                config_class = AutoConfig.for_model(
                    model_type=value["model_type"]
                ).__class__
            return config_class(**value)
        return value

    @property
    def target_vocab_size(self) -> int:
        """Get target vocabulary size from transformer config."""
        return self.transformer_layer_config.vocab_size