sg$fdZddlmZddlmZddlmZejeZ GddeZ y)zGraniteMoe model configuration)PretrainedConfig)rope_config_validation)loggingcdeZdZdZdZdgZ dfd ZxZS)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 ``` granitemoepast_key_valuesc ||_||_||_||_||_||_||}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_||_||_||_||_||_||_||_||_t/|`d| | ||d|t3|y)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_theta rope_scalingattention_biasattention_dropoutembedding_multiplierlogits_scalingresidual_multiplierattention_multipliernum_local_expertsnum_experts_per_tokoutput_router_logitsrouter_aux_loss_coefsuper__init__r)selfrrrrrrrrrrrr r r rrrrrrr 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(zGraniteMoeConfig.__init__xs>%'>$&!2!2#6   &"5 #6 $!2("$(,!2$8!,#6 $8!!2#6 $8!$8!  %%% 3     t$)i}ii+ r.Nsiluig{Gz?gư>TNFg@NFg?r2r2r2r1FgMbP?)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer( __classcell__)r+s@r,rrsyT lJ#4"5  $!  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