
    sg$                     f    d Z ddlmZ ddlmZ ddlmZ  ej                  e      Z	 G d de      Z
y)zGraniteMoe model configuration   )PretrainedConfig)rope_config_validation)loggingc                   d     e Zd ZdZdZdgZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )GraniteMoeConfiga.  
    This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
    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 GraniteMoe-3B.

    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 32000):
            Vocabulary size of the GraniteMoe model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GraniteMoeModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        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*):
            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 2048):
            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-06):
            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*):
            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 `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`, *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.
        embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
        logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
        residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
        attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
        num_local_experts (`int`, *optional*, defaults to 8): total number of experts
        num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
        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.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxialiary loss coefficient

    ```python
    >>> from transformers import GraniteMoeModel, GraniteMoeConfig

    >>> # Initializing a GraniteMoe granitemoe-3b style configuration
    >>> configuration = GraniteMoeConfig()

    >>> # Initializing a model from the granitemoe-7b style configuration
    >>> model = GraniteMoeModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
granitemoepast_key_valuesc                    || _         || _        || _        || _        || _        || _        ||}|| _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        t/        | `  d||||d| t3        |        y )N)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embedding_multiplierlogits_scalingresidual_multiplierattention_multipliernum_local_expertsnum_experts_per_tokoutput_router_logitsrouter_aux_loss_coefsuper__init__r   )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$   r%   r&   kwargs	__class__s                                j/var/www/html/venv/lib/python3.12/site-packages/transformers/models/granitemoe/configuration_granitemoe.pyr(   zGraniteMoeConfig.__init__x   s    > %'>$&!2!2#6  &"5#6 $!2("$(,!2$8!,#6 $8!!2#6 $8!$8! 	
%%% 3		

 	
 	t$    )i }  i   i +      r.   Nsilui   g{Gz?gư>TN      Fg     @NFg              ?r2   r2   r2      r1   FgMbP?)__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferencer(   __classcell__)r+   s   @r,   r   r      sy    Tl J#4"5   $!  ""9G% G%r-   r   N)r7   configuration_utilsr   modeling_rope_utilsr   utilsr   
get_loggerr4   loggerr   r   r-   r,   <module>r@      s6   ( % 3 9  
		H	%a%' a%r-   