# Copyright 2025 Arcee AI and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """AFMoE model configuration""" from ...configuration_utils import PreTrainedConfig, layer_type_validation from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class AfmoeConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`AfmoeModel`]. It is used to instantiate an AFMoE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of [arcee-ai/Trinity-Mini](https://huggingface.co/arcee-ai/Trinity-Mini). AFMoE is an Adaptive Feedforward MoE (Mixture of Experts) model with token-choice routing, shared experts, and a hybrid attention mechanism combining sliding window and full attention patterns. 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 200192): Vocabulary size of the AFMoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AfmoeModel`]. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6144): Dimension of the dense MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1408): Intermediate size of the routed expert MLPs. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_dense_layers (`int`, *optional*, defaults to 1): Number of initial dense layers before MoE layers begin. Layers with index < num_dense_layers will use standard dense MLPs instead of MoE. num_attention_heads (`int`, *optional*, defaults to 16): 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 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`. head_dim (`int`, *optional*, defaults to 128): The dimension of each attention head. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the MLP blocks. max_position_embeddings (`int`, *optional*, defaults to 16384): The maximum sequence length that this model might ever be used with. 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 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. num_experts (`int`, *optional*, defaults to 64): Number of routed experts in MoE layers. num_experts_per_tok (`int`, *optional*, defaults to 6): Number of experts to route each token to. This is the top-k value for the token-choice routing. num_shared_experts (`int`, *optional*, defaults to 2): Number of shared experts that are always activated for all tokens. route_scale (`float`, *optional*, defaults to 1.0): Scaling factor applied to routing weights. global_attn_every_n_layers (`int`, *optional*, defaults to 4): The frequency of full attention layers. Every Nth layer will use full attention, while others use sliding window attention. sliding_window (`int`, *optional*, defaults to 1024): Sliding window size for local attention layers. layer_types (`list[str]`, *optional*): A list that explicitly maps each layer index with its attention type. Each element should be either "sliding_attention" or "full_attention". If not provided, it will be automatically generated based on `global_attn_every_n_layers`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mup_enabled (`bool`, *optional*, defaults to `False`): Whether to enable muP (Maximal Update Parametrization) input scaling. When enabled, input embeddings are scaled by `sqrt(hidden_size)`. eos_token_id (`int`, *optional*): End of stream token id. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. Example: ```python >>> from transformers import AfmoeModel, AfmoeConfig >>> # Initializing an AFMoE configuration >>> configuration = AfmoeConfig() >>> # Initializing a model from the afmoe-small-sft-v1 style configuration >>> model = AfmoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "afmoe" keys_to_ignore_at_inference = ["past_key_values"] # Default pipeline parallel plan for base model 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: int | None = 200192, hidden_size: int | None = 2048, intermediate_size: int | None = 6144, moe_intermediate_size: int | None = 1408, num_hidden_layers: int | None = 32, num_dense_layers: int | None = 1, num_attention_heads: int | None = 16, num_key_value_heads: int | None = None, head_dim: int | None = 128, hidden_act: str | None = "silu", max_position_embeddings: int | None = 16384, initializer_range: float | None = 0.02, rms_norm_eps: float | None = 1e-5, use_cache: bool | None = True, tie_word_embeddings: bool | None = False, rope_theta: float | None = 10000.0, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, num_experts: int | None = 64, num_experts_per_tok: int | None = 6, num_shared_experts: int | None = 2, route_scale: float | None = 1.0, global_attn_every_n_layers: int | None = 4, sliding_window: int | None = 1024, layer_types: list | None = None, attention_dropout: float | None = 0.0, mup_enabled: bool | None = False, eos_token_id: bool | None = None, pad_token_id: bool | None = None, bos_token_id: bool | None = None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_dense_layers = num_dense_layers self.num_attention_heads = num_attention_heads self.head_dim = head_dim self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_parameters = rope_parameters # MoE specific self.moe_intermediate_size = moe_intermediate_size self.num_experts_per_tok = num_experts_per_tok self.num_experts = num_experts self.num_shared_experts = num_shared_experts self.route_scale = route_scale self.attention_bias = False # Attention specific self.attention_dropout = attention_dropout self.global_attn_every_n_layers = global_attn_every_n_layers self.sliding_window = sliding_window self.mup_enabled = mup_enabled self.layer_types = layer_types if self.layer_types is None: self.layer_types = [ "sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["AfmoeConfig"]