i!<dZddlmZddlmZGddeZdgZy)zOLMoE model configuration)PreTrainedConfig)RopeParametersc2heZdZdZdZdgZddiZ ddedzdedzd edzd edzd edzd edzd edzdedzde dzdedzde dzdedzdedzdedzdedzde e ee fzdzde dzde dzde dzdedzdedzde dzde dzde dzf0fd Z xZS) OlmoeConfigas This is the configuration class to store the configuration of a [`OlmoeModel`]. It is used to instantiate an OLMoE 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 [allenai/OLMoE-1B-7B-0924](https://huggingface.co/allenai/OLMoE-1B-7B-0924). 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 50304): Vocabulary size of the OLMoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OlmoeModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 2048): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 16): Number of hidden layers in the Transformer decoder. 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`. 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 4096): 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`. pad_token_id (`int`, *optional*, defaults to 1): Padding token id. bos_token_id (`int`, *optional*): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 50279): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight 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`. attention_bias (`bool`, defaults to `False`, *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. clip_qkv (`float`, *optional*): If not `None`, elements of query, key and value attention states are clipped so that their absolute value does not exceed this value. num_experts_per_tok (`int`, *optional*, defaults to 8): Number of selected experts. num_experts (`int`, *optional*, defaults to 64): Number of routed experts. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss, including load balancing loss and router z-loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.01): The aux loss factor for the total loss. norm_topk_prob (`bool`, *optional*, defaults to `False`): Whether to normalize the topk probabilities. ```python >>> from transformers import OlmoeModel, OlmoeConfig >>> # Initializing a OLMoE 7B A1B style configuration >>> configuration = OlmoeConfig() >>> # Initializing a model from the OLMoE 7B A1B style configuration >>> model = OlmoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```olmoepast_key_valuesnum_local_experts num_expertsN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutclip_qkvnum_experts_per_tokoutput_router_logitsrouter_aux_loss_coefnorm_topk_probc |||_||_||_||_||_||_||}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_||_||_||_||_||_||_| |_| |_||_t1|ddi|y)N)r rr r rrrrrrrrrrrr rr r!rrrrrsuper__init__)selfr r r rrrrrrrrrrrrrrrrrr rr r!kwargs __class__s i/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/olmoe/configuration_olmoe.pyr%zOlmoeConfig.__init__ks8%'>$&!2!2#6   &"5 #6 $!2(",!2  #6 &$8!$8!,.#6 ((( "6")ir+r,Nsiluig{Gz?gh㈵>TNigFNFgN@Fg{Gz?F)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference attribute_mapintstrfloatboolrdictr% __classcell__)r(s@r)rrsR hJ#4"5(-8M"'"&(,(**,*.!'.2*.#(!%#$#'#(*/MQ&+*- $*+"$,1-1&+3:#$J:#4Z:#: :# : :# !4Z :#!4Z:#$J:#"%t:#!4<:#Dj:#$;:#Dj:#Dj:#Dj:# !4Z!:#"($sN/B*CCdJ#:#$t %:#&!4<':#(+):#*!4Z+:#,4Z-:#.#Tk/:#0$dl1:#2t 3:#:#r*rN)r4configuration_utilsrmodeling_rope_utilsrr__all__r#r*r)rAs) 31S#"S#l /r*