qi%ldZddlmZddlmZddlmZejeZ GddeZ dgZ y)z$GraniteMoeShared model configuration)PreTrainedConfig)RopeParameters)loggingc8eZdZdZdZdgZ d!dedzdedzdedzdedzd edzd edzd edzd edzd edzdedzde dzdedzdedzdedzde dzde e ee fzdzde dzdedzdedzdedzdedzdedzdedzdedzde dzdedzdedzf6fd Z xZ S)"GraniteMoeSharedConfigaO This is the configuration class to store the configuration of a [`GraniteMoeSharedModel`]. It is used to instantiate an GraniteMoeShared 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 [ibm-research/moe-7b-1b-active-shared-experts](https://huggingface.co/ibm-research/moe-7b-1b-active-shared-experts). 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 GraniteMoeShared model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GraniteMoeSharedModel`] 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, 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 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_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`, *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. Enabling this will also allow the model to output the auxiliary loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxiliary loss coefficient shared_intermediate_size (`int`, *optional*, defaults to 0): intermediate size for shared experts. 0 implies no shared experts. ```python >>> from transformers import GraniteMoeSharedModel, GraniteMoeSharedConfig >>> # Initializing a GraniteMoeShared granitemoe-3b style configuration >>> configuration = GraniteMoeSharedConfig() >>> # Initializing a model from the granitemoe-7b style configuration >>> model = GraniteMoeSharedModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```granitemoesharedpast_key_valuesN 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_dropoutembedding_multiplierlogits_scalingresidual_multiplierattention_multipliernum_local_expertsnum_experts_per_tokoutput_router_logitsrouter_aux_loss_coefshared_intermediate_sizec ||_||_||_||_||_||_||}||_||_| |_| |_ | |_ ||_ ||_ ||_ ||_||_||_||_||_||_||_||_||_| |_| |_||_d|_||_t9|tdi|y)Nrope)r rr r r rrrrrrrrrrrrr r!r"r#r$rrrrposition_embedding_typersuper__init__)selfr r r r rrrrrrrrrrrrrrrrrrr r!r"r#r$kwargs __class__s u/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/granitemoeshared/configuration_granitemoeshared.pyr*zGraniteMoeSharedConfig.__init__ss>%'>$&!2!2#6   &"5 #6 $!2(",!2$8!,#6 $8!!2#6 $8!$8!(@%#6 ((((.$. "6")i}ii+ r0Nsiluig{Gz?gư>TNFNFg?r4r4r4r3FgMbP?)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferenceintstrfloatboolrdictr* __classcell__)r-s@r.rrsP d$J#4"5"'"&(-(**,*.!'.2*.#'!%#'#$#$+0MQ&+*--0'*,/-0()*+,1-2/09F#$JF#4ZF#: F# : F# !4Z F#!4ZF#$JF#"%tF#!4<F#DjF#$;F#DjF#DjF#DjF# "D[!F#"($sN/B*CCdJ#F#$t %F#&!4<'F#($dl)F#* +F#,#T\-F#.$dl/F#0:1F#2!4Z3F#4#Tk5F#6$dl7F#8#&*9F#F#r/rN) r:configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr7loggerr__all__r'r/r.rIsA&+31   H %\#-\#~ $ $r/