qizldZddlmZddlmZddlmZejeZ GddeZ dgZ y)zJetMoe model configuration)PreTrainedConfig)RopeParameters)loggingc,DeZdZdZdZdgZddiZ ddedzdedzd edzd edzdedzd edzd edzd edzdedzdedzde dzde dzde dzdedzdedzdedzde dzde e ee fzdzdedzde dzde dzf*fd Z xZS) JetMoeConfigax This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a configuration of the JetMoe-4B. [jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b) 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 JetMoe model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`JetMoeModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 16): Number of attention heads for each key and value in the Transformer encoder. kv_channels (`int`, *optional*, defaults to 128): Defines the number of channels for the key and value tensors. intermediate_size (`int`, *optional*, defaults to 5632): Dimension of the MLP representations. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of up to 4096 tokens. activation_function (`string`, *optional*, defaults to `"silu"`): Defines the activation function for MLP experts. num_local_experts (`int`, *optional*, defaults to 8): Defines the number of experts in the MoE and MoA. num_experts_per_tok (`int, *optional*, defaults to 2): The number of experts to route per-token and for MoE and MoA. 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. aux_loss_coef (`float`, *optional*, defaults to 0.01): The coefficient for the auxiliary loss. 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`. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. pad_token_id (`int`, *optional*): The id of the padding token. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether the model's input and output word embeddings should be tied. 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`. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. initializer_range (`float`, *optional*, defaults to 0.01): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import JetMoeModel, JetMoeConfig >>> # Initializing a JetMoe 4B style configuration >>> configuration = JetMoeConfig() >>> # Initializing a model from the JetMoe 4B style configuration >>> model = JetMoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```jetmoepast_key_valueshead_dim kv_channelsN vocab_size hidden_sizenum_hidden_layersnum_key_value_headsintermediate_sizemax_position_embeddingsactivation_functionnum_local_expertsnum_experts_per_tokoutput_router_logits aux_loss_coef use_cache bos_token_id eos_token_id pad_token_idtie_word_embeddingsrope_parameters rms_norm_epsinitializer_rangeattention_dropoutc ~| | kDr td||_||_||_|| z|_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_t/|`di|y)NzG`num_experts_per_tok` must be less than or equal to `num_local_experts`) ValueErrorr r rnum_attention_headsrr rrrrrrrrrrrrrrrrsuper__init__)selfr r rrr rrrrrrrrrrrrrrrrkwargs __class__s a/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/jetmoe/configuration_jetmoe.pyr%zJetMoeConfig.__init__gs2 !2 2fg g$&!2#69L#L #6 &!2'>$#6 !2#6 $8!*"!2!2((((.#6  "6")i}i iisiluF{Gz?Tr0NTNgư>r1g)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inference attribute_mapintstrboolfloatrdictr% __classcell__)r(s@r)rrsH TJ#4"5/M"'"&(**,"%(,.2*0()*+,1&*!%#$#$#'+/MQ#'*.*--2#$J2#4Z2#: 2# !4Z 2# 4Z 2#:2#"%t2#!4Z2#:2#!4Z2##Tk2#t|2#$;2#Dj2# Dj!2#"Dj#2#$"D[%2#&($sN/B*CCdJ'2#(Dj)2#*!4<+2#,!4<-2#2#r*rN) r6configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr3loggerr__all__r!r*r)rFsA!31   H %A##A#H  r*