# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from typing import Any from transformers.configuration_utils import PretrainedConfig class Lfm2MoeConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Lfm2MoeModel`]. It is used to instantiate a LFM2 Moe model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LFM2-8B-A1B model. e.g. [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B) 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 65536): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Lfm2Model`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 7168): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1792): Intermediate size of the routed expert. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. pad_token_id (`int`, *optional*, defaults to 0): 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 `True`): Whether to tie weight embeddings rope_parameters (`dict`, *optional*): The parameters of the RoPE embeddings. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. 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`. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the conv layers. conv_L_cache (`int`, *optional*, defaults to 3): L_cache dim in the conv layers. num_dense_layers (`int`, *optional*, defaults to 2): Number of dense Lfm2MoeMLP layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). num_experts_per_tok (`int`, *optional*, defaults to 4): Number of selected experts. num_experts (`int`, *optional*, defaults to 32): Number of routed experts. use_expert_bias (`bool`, *optional*, defaults to `True`): Whether to use the expert bias on the routing weights. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor for routed experts in MoE models. norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize the topk probabilities. layer_types (`Optional`, *optional*): Type of each layers. ```python >>> from transformers import Lfm2MoeModel, Lfm2MoeConfig >>> # Initializing a LFM2 Moe model >>> configuration = Lfm2MoeConfig() >>> # Initializing a model from the LFM2-8B-A1B style configuration >>> model = Lfm2MoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" # noqa: E501 model_type = "lfm2_moe" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int = 65536, hidden_size: int = 2048, intermediate_size: int = 7168, moe_intermediate_size: int = 1792, num_hidden_layers: int = 32, pad_token_id: int = 0, bos_token_id: int = 1, eos_token_id: int = 2, tie_word_embeddings: bool = True, rope_parameters: dict[str, Any] | None = None, max_position_embeddings: int = 128_000, use_cache: bool = True, norm_eps: float = 0.00001, num_attention_heads: int = 32, num_key_value_heads: int = 8, conv_bias: bool = False, conv_L_cache: int = 3, num_dense_layers: int = 2, num_experts_per_tok: int = 4, num_experts: int = 32, use_expert_bias: bool = True, routed_scaling_factor: float = 1.0, norm_topk_prob: bool = True, layer_types: list[str] | None = None, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers rope_theta = kwargs.pop("rope_theta", 1000000.0) if rope_parameters is None: rope_parameters = {"rope_type": "default", "rope_theta": rope_theta} self.rope_parameters = rope_parameters self.max_position_embeddings = max_position_embeddings self.use_cache = use_cache self.norm_eps = norm_eps # attn operator config self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads # custom operator config self.conv_bias = conv_bias self.conv_L_cache = conv_L_cache # moe config self.num_dense_layers = num_dense_layers self.moe_intermediate_size = moe_intermediate_size self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.use_expert_bias = use_expert_bias self.routed_scaling_factor = routed_scaling_factor self.norm_topk_prob = norm_topk_prob self.layer_types = layer_types tie_word_embeddings = kwargs.get( "tie_embedding", tie_word_embeddings ) # to fit original config keys 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__ = ["Lfm2MoeConfig"]