configuration_nandi.py

from transformers.configuration_utils import PretrainedConfig


class NandiConfig(PretrainedConfig):
    r"""
    Configuration class for the Nandi model.

    Example:

    ```python
    >>> from transformers import AutoConfig, AutoModelForCausalLM

    >>> configuration = AutoConfig.from_pretrained("Rta-AILabs/Nandi-500M-remote", trust_remote_code=True)

    >>> model = AutoModelForCausalLM.from_pretrained("Rta-AILabs/Nandi-500M-remote", trust_remote_code=True)

    >>> configuration = model.config
    ```
    """

    model_type = "nandi"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }

    def __init__(
        self,
        vocab_size=131072,
        hidden_size=1248,
        intermediate_size=3556,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=8,
        head_dim=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.008,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=0,
        pretraining_tp=1,
        tie_word_embeddings=True,
        rope_parameters=None,
        attention_bias=False,
        attention_dropout=0.0,
        mlp_bias=False,
        factorized_embedding=False,
        embedding_rank=768,
        layer_sharing=False,
        layer_sharing_repeats=1,
        qk_norm=True,
        shared_kv=True,
        kv_cache_mode="shared",
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
        self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.pretraining_tp = pretraining_tp
        self.rope_parameters = rope_parameters if rope_parameters is not None else {"rope_theta": 1000000.0}
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.factorized_embedding = factorized_embedding
        self.embedding_rank = embedding_rank
        self.layer_sharing = layer_sharing

        self.layer_sharing_repeats = max(1, int(layer_sharing_repeats or 1))
        self.qk_norm = qk_norm

        self.shared_kv = shared_kv

        if kv_cache_mode not in ("shared", "vanilla"):
            raise ValueError(
                f"`kv_cache_mode` must be 'shared' or 'vanilla', got {kv_cache_mode!r}."
            )
        self.kv_cache_mode = kv_cache_mode

        if self.factorized_embedding and self.embedding_rank <= 0:
            raise ValueError(
                f"`embedding_rank` must be positive when `factorized_embedding=True`, got {self.embedding_rank}."
            )
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"`hidden_size` ({self.hidden_size}) must be divisible by "
                f"`num_attention_heads` ({self.num_attention_heads})."
            )
        if self.layer_sharing_repeats < 1:
            raise ValueError(f"`layer_sharing_repeats` must be >= 1, got {self.layer_sharing_repeats}.")

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["NandiConfig"]