
    sgL#                     6    d Z ddlmZ ddlmZ  G d de      Zy)zOLMoE model configuration   )PretrainedConfig)rope_config_validationc                   `     e Zd ZdZdZdgZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )OlmoeConfiga  
    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 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 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_theta (`float`, *optional*, defaults to 10000.0):
            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}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        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. Enabeling 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
    ```olmoepast_key_valuesc                    || _         || _        || _        || _        || _        || _        ||}|| _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        | j                  *d| j                  v r| j                  d   | j                  d<   t+        |        t-        | \  d||||d| y )Ntype	rope_type)pad_token_idbos_token_ideos_token_idtie_word_embeddings )
vocab_sizemax_position_embeddingshidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads
hidden_actinitializer_rangerms_norm_eps	use_cache
rope_thetarope_scalingattention_biasattention_dropoutclip_qkvnum_experts_per_toknum_expertsoutput_router_logitsrouter_aux_loss_coefnorm_topk_probr   super__init__)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   kwargs	__class__s                              `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/olmoe/configuration_olmoe.pyr'   zOlmoeConfig.__init__p   s   : %'>$&!2!2#6  &"5#6 $!2("$(,!2 #6 &$8!$8!, (Vt7H7H-H-1->->v-FDk*t$ 	
%%% 3		

 	
    )i     r-      r.   Nsilui   g{Gz?gh㈵>T   Nig  Fg     @NFg        N   @   Fg{Gz?F)__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferencer'   __classcell__)r*   s   @r+   r   r      ss    Xt J#4"5   $!"!5C
 C
r,   r   N)r6   configuration_utilsr   modeling_rope_utilsr   r   r   r,   r+   <module>r<      s      3 9a
" a
r,   