configuration_modernbert_small.py

from __future__ import annotations

from typing import Optional

from transformers import PretrainedConfig

from babylm.config.schema import ModelConfig


class ModernBertSmallConfig(PretrainedConfig):
    """HF-compatible config for the from-scratch ModernBERT-Small architecture.

    Bridges to the pydantic `ModelConfig` (used for JSON validation/CLI) via `to_hf_config`,
    while giving `save_pretrained`/`from_pretrained`/`AutoConfig` compatibility.
    """

    model_type = "modernbert_small"

    def __init__(
        self,
        vocab_size: int = 30522,
        hidden_size: int = 384,
        num_hidden_layers: int = 16,
        num_attention_heads: int = 6,
        intermediate_size: int = 576,
        hidden_activation: str = "gelu",
        attention_pattern: str = "alternating",
        global_attn_every_n_layers: int = 3,
        global_rope_theta: float = 160000.0,
        local_rope_theta: float = 10000.0,
        local_attention_window: int = 128,
        norm_eps: float = 1e-5,
        attention_dropout: float = 0.0,
        mlp_dropout: float = 0.0,
        embedding_dropout: float = 0.0,
        embedding_type: str = "standard",
        factorized_linear_bottleneck_dim: int = 128,
        factorized_mlp_bottleneck_dim: int = 128,
        sttp_rank: int = 8,
        sttp_num_factors: int = 3,
        pete_basis_dim: int = 384,
        compositional_bottleneck_dim: int = 128,
        compositional_num_buckets: int = 8192,
        compositional_min_ngram: int = 1,
        compositional_max_ngram: int = 4,
        compositional_residual_mode: str = "none",
        compositional_frequency_tau: float = 100.0,
        compositional_frequency_shuffle_seed: int = 42,
        geometry_penalty_mode: str = "none",
        geometry_penalty_weight: float = 0.01,
        geometry_sample_size: int = 2048,
        geometry_sampling_mode: str = "zipfian",
        compositional_token_strings: Optional[list[str]] = None,
        token_frequencies: Optional[list[int]] = None,
        special_token_ids: Optional[list[int]] = None,
        initializer_range: float = 0.02,
        pad_token_id: int = 0,
        tie_word_embeddings: bool = True,
        **kwargs,
    ) -> None:
        super().__init__(pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_activation = hidden_activation
        self.attention_pattern = attention_pattern
        self.global_attn_every_n_layers = global_attn_every_n_layers
        self.global_rope_theta = global_rope_theta
        self.local_rope_theta = local_rope_theta
        self.local_attention_window = local_attention_window
        self.norm_eps = norm_eps
        self.attention_dropout = attention_dropout
        self.mlp_dropout = mlp_dropout
        self.embedding_dropout = embedding_dropout
        self.embedding_type = embedding_type
        self.factorized_linear_bottleneck_dim = factorized_linear_bottleneck_dim
        self.factorized_mlp_bottleneck_dim = factorized_mlp_bottleneck_dim
        self.sttp_rank = sttp_rank
        self.sttp_num_factors = sttp_num_factors
        self.pete_basis_dim = pete_basis_dim
        self.compositional_bottleneck_dim = compositional_bottleneck_dim
        self.compositional_num_buckets = compositional_num_buckets
        self.compositional_min_ngram = compositional_min_ngram
        self.compositional_max_ngram = compositional_max_ngram
        self.compositional_residual_mode = compositional_residual_mode
        self.compositional_frequency_tau = compositional_frequency_tau
        self.compositional_frequency_shuffle_seed = compositional_frequency_shuffle_seed
        self.geometry_penalty_mode = geometry_penalty_mode
        self.geometry_penalty_weight = geometry_penalty_weight
        self.geometry_sample_size = geometry_sample_size
        self.geometry_sampling_mode = geometry_sampling_mode
        self.compositional_token_strings = compositional_token_strings
        self.token_frequencies = token_frequencies
        self.special_token_ids = special_token_ids or []
        self.initializer_range = initializer_range


def to_hf_config(model_cfg: ModelConfig, pad_token_id: Optional[int] = None) -> ModernBertSmallConfig:
    """Builds a ModernBertSmallConfig from the pydantic ModelConfig, optionally overriding
    pad_token_id with the value discovered from a trained tokenizer."""
    data = model_cfg.model_dump()
    if pad_token_id is not None:
        data["pad_token_id"] = pad_token_id
    return ModernBertSmallConfig(**data)