o ¾e¦iU*ã@sPdZddlmZmZddlmZddlmZe e ¡Z Gdd„deƒZ dgZ dS)zAFMoE model configurationé)ÚPreTrainedConfigÚlayer_type_validation)ÚRopeParameters)Úloggingc<s‚eZdZdZdZdgZdgdgfddgdgfdgdgfdœZ                        d?d edBd!edBd"edBd#edBd$edBd%edBd&edBd'edBd(edBd)edBd*edBd+e dBd,e dBd-e dBd.e dBd/e dBd0e e ee fBdBd1edBd2edBd3edBd4e dBd5edBd6edBd7e dBd8e dBd9e dBd:e dBd;e dBd„ Z‡ZS)@Ú AfmoeConfiga® 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 ``` ÚafmoeÚpast_key_valuesÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnorméééé€é ééNé€Úsilué@ç{®Gáz”?çñh㈵øä>TFçˆÃ@é@ééçð?ééçÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚmoe_intermediate_sizeÚnum_hidden_layersÚnum_dense_layersÚnum_attention_headsÚnum_key_value_headsÚhead_dimÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ rms_norm_epsÚ use_cacheÚtie_word_embeddingsÚ rope_thetaÚrope_parametersÚ num_expertsÚnum_experts_per_tokÚnum_shared_expertsÚ route_scaleÚglobal_attn_every_n_layersÚsliding_windowÚ layer_typesÚattention_dropoutÚ mup_enabledÚ eos_token_idÚ pad_token_idÚ bos_token_idc s||_| |_||_||_||_||_||_| |_| |_| |_ | |_ ||_ ||_ ||_ ||_||_||_||_||_d|_||_ˆ|_||_||_||_|jdur]‡fdd„t|jƒDƒ|_t|jƒ|durh|}||_||_||_||_||_t ƒj!di|¤ŽdS)NFcs$g|]}t|dˆƒrdnd‘qS)rÚsliding_attentionÚfull_attention)Úbool)Ú.0Úi©r9©úk/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/afmoe/configuration_afmoe.pyÚ Çsÿÿz(AfmoeConfig.__init__..rG)"r$r.r%r&r(r)r*r,r-r/r0r1r3r4r'r6r5r7r8Úattention_biasr<r9r:r=r;Úrangerr+r>r?r@r2ÚsuperÚ__init__)Úselfr$r%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@Úkwargs©Ú __class__rFrHrMˆsL!  þ zAfmoeConfig.__init__)rrrrrrrNrrrrrTFrNrrrr r!r"Nr#FNNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_pp_planÚintÚstrÚfloatrCrÚdictÚlistrMÚ __classcell__rGrGrPrHrsÀe   ýâþýüûúùø ÷ ö õ ô óòñðïîíìëêéèçæåäãârN) rUÚconfiguration_utilsrrÚmodeling_rope_utilsrÚutilsrÚ get_loggerrRÚloggerrÚ__all__rGrGrGrHÚs     B