configuration_longcat_ngram.py

from transformers.models.longcat_flash import LongcatFlashConfig


class LongcatFlashNgramConfig(LongcatFlashConfig):
    r"""
    This is the configuration class to store the configuration of a [`LongcatFlashNgramModel`]. It is used to instantiate
    a LongCat Flash model with N-gram enhanced embeddings according to the specified arguments, defining the model architecture. 
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 131072):
            Vocabulary size of the LongCat Flash model. Defines the number of different tokens that can be represented by the
            `input_ids` passed when calling [`LongcatFlashNgramModel`]
        hidden_size (`int`, *optional*, defaults to 6144):
            Dimension of the hidden representations.
        num_hidden_layers (`int`, *optional*, defaults to 56):
            Number of hidden layers in the Transformer decoder.
        num_layers (`int`, *optional*, defaults to 28):
            Number of layers, each with 2 sublayers.
        num_attention_heads (`int`, *optional*, defaults to 64):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting from a multi-head checkpoint to a GQA checkpoint, each group key and value head should be
            constructed by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 131072):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon value used by the RMS normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie input and output embeddings.
        rope_theta (`float`, *optional*, defaults to 10000000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        ffn_hidden_size (`int`, *optional*, defaults to 12288):
            Dimension of the MLP representations.
        q_lora_rank (`int`, *optional*, defaults to 1536):
            The rank of the query LoRA projection in MLA (Multi-head Latent Attention).
        kv_lora_rank (`int`, *optional*, defaults to 512):
            The rank of the key-value LoRA projection in MLA.
        qk_nope_head_dim (`int`, *optional*, defaults to 128):
            The dimension of the non-position encoding part of query/key heads.
        qk_rope_head_dim (`int`, *optional*, defaults to 64):
            The dimension of the RoPE part of query/key heads.
        head_dim (`int`, *optional*, defaults to 64):
            Standard dimension of qk heads, unused except for CI.
        v_head_dim (`int`, *optional*, defaults to 128):
            The dimension of value heads.
        qk_head_dim (`int`, *optional*):
            The total dimension of query/key heads. If not specified, set to `qk_nope_head_dim + qk_rope_head_dim`.
        moe_topk (`int`, *optional*, defaults to 12):
            Number of experts to route to for each token in the MoE layer.
        n_routed_experts (`int`, *optional*, defaults to 512):
            Number of routed experts in the MoE layer.
        zero_expert_num (`int`, *optional*, defaults to 256):
            Number of zero experts (identity function) to add to the expert pool.
        expert_ffn_hidden_size (`int`, *optional*, defaults to 2048):
            Hidden size of individual expert FFN layers.
        routed_scaling_factor (`float`, *optional*, defaults to 6.0):
            Scaling factor applied to the routing weights.
        emb_neighbor_num (`int`, *optional*):
            Maximum N-gram length for N-gram embeddings. This parameter determines the context window size for N-gram computation. Higher values capture 
            longer-range lexical patterns but increase memory usage.
        emb_split_num (`int`, *optional*):
            Number of hash functions (or splits) to use for N-gram embeddings. Multiple hash functions help improve the quality of N-gram representations. 
        ngram_vocab_size_ratio (`float`, *optional*):
            Ratio multiplier for N-gram vocabulary size relative to the base vocabulary size. The N-gram vocabulary 
            size is calculated as `vocab_size * ngram_vocab_size_ratio`. 

    Example:
    ```python
    >>> from transformers import LongcatFlashNgramModel, LongcatFlashNgramConfig

    >>> # Initializing a LongCat Flash N-gram style configuration
    >>> configuration = LongcatFlashNgramConfig(
    ...     emb_neighbor_num=3,
    ...     emb_split_num=4,
    ...     ngram_vocab_size_ratio=1.5
    ... )

    >>> # Initializing a model from the configuration
    >>> model = LongcatFlashNgramModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "longcat_flash_ngram"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.*.q_b_proj": "colwise",
        "layers.*.self_attn.*.kv_b_proj": "colwise",
        "layers.*.self_attn.*.o_proj": "rowwise",
        "layers.*.mlps.*.gate_proj": "colwise",
        "layers.*.mlps.*.up_proj": "colwise",
        "layers.*.mlps.*.down_proj": "rowwise",
        "layers.*.mlp.experts.*.gate_proj": "colwise",
        "layers.*.mlp.experts.*.up_proj": "colwise",
        "layers.*.mlp.experts.*.down_proj": "rowwise",
    }

    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        vocab_size=131072,
        hidden_size=6144,
        num_hidden_layers=56,
        num_layers=28,
        num_attention_heads=64,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=131072,
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=10000000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        ffn_hidden_size=12288,
        q_lora_rank=1536,
        kv_lora_rank=512,
        qk_nope_head_dim=128,
        qk_rope_head_dim=64,
        head_dim=64,
        v_head_dim=128,
        qk_head_dim=None,
        moe_topk=12,
        n_routed_experts=512,
        zero_expert_num=256,
        expert_ffn_hidden_size=2048,
        routed_scaling_factor=6.0,
        emb_neighbor_num=None,
        emb_split_num=None,
        ngram_vocab_size_ratio=None,
        **kwargs,
    ):
        # N-gram embedding specific parameters
        self.emb_neighbor_num = emb_neighbor_num
        self.emb_split_num = emb_split_num
        self.ngram_vocab_size_ratio = ngram_vocab_size_ratio

        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_layers=num_layers,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            initializer_range=initializer_range,
            rms_norm_eps=rms_norm_eps,
            use_cache=use_cache,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            attention_bias=attention_bias,
            attention_dropout=attention_dropout,
            ffn_hidden_size=ffn_hidden_size,
            q_lora_rank=q_lora_rank,
            kv_lora_rank=kv_lora_rank,
            qk_nope_head_dim=qk_nope_head_dim,
            qk_rope_head_dim=qk_rope_head_dim,
            head_dim=head_dim,
            v_head_dim=v_head_dim,
            qk_head_dim=qk_head_dim,
            moe_topk=moe_topk,
            n_routed_experts=n_routed_experts,
            zero_expert_num=zero_expert_num,
            expert_ffn_hidden_size=expert_ffn_hidden_size,
            routed_scaling_factor=routed_scaling_factor,
            **kwargs,
        )


__all__ = ["LongcatFlashNgramConfig"]