configuration_metallm.py

"""HF config for MetaLLM / Vishvakarma (custom arch: NoPE-every-N, QK-norm, GQA, SwiGLU).

Field names intentionally mirror configs/model.py:ModelConfig so this object can be
passed directly to metallm_core.MetaLLMv2 as its `cfg` (duck-typed).
"""
from transformers import PretrainedConfig


class MetaLLMConfig(PretrainedConfig):
    model_type = "metallm"

    def __init__(
        self,
        vocab_size: int = 40000,
        n_layers: int = 26,
        d_model: int = 1792,
        n_heads: int = 28,
        n_kv_heads: int = 14,
        d_ff: int = 4864,
        max_seq_len: int = 2048,
        rope_theta: float = 500_000.0,
        norm_eps: float = 1e-5,
        tie_embeddings: bool = True,
        qk_norm: bool = True,
        z_loss_weight: float = 0.0,   # inference shim: aux loss unused, kept for fidelity
        rope_fp32: bool = True,
        doc_mask: bool = True,        # equals plain causal for single-document prompts
        attn_impl: str = "sdpa",      # "sdpa" is the portable inference default
        nope_every: int = 4,
        bos_id: int = 1,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.n_layers = n_layers
        self.d_model = d_model
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.d_ff = d_ff
        self.max_seq_len = max_seq_len
        self.rope_theta = rope_theta
        self.norm_eps = norm_eps
        self.tie_embeddings = tie_embeddings
        self.qk_norm = qk_norm
        self.z_loss_weight = z_loss_weight
        self.rope_fp32 = rope_fp32
        self.doc_mask = doc_mask
        self.attn_impl = attn_impl
        self.nope_every = nope_every
        self.bos_id = bos_id
        # standard-name aliases: transformers>=5.13 core reads these directly
        self.num_hidden_layers = n_layers
        self.hidden_size = d_model
        self.num_attention_heads = n_heads
        self.num_key_value_heads = n_kv_heads
        self.max_position_embeddings = max_seq_len
        kwargs.setdefault("tie_word_embeddings", tie_embeddings)
        kwargs.setdefault("bos_token_id", bos_id)
        super().__init__(**kwargs)

    @property
    def head_dim(self) -> int:
        return self.d_model // self.n_heads