o Zhk#@s@dZddlmZddlmZeeZGdddeZdgZ dS)zMixtral model configuration)PretrainedConfig)loggingc seZdZdZdZdgZdddddddddZdgd gfd d gd gfd gd gfd Z                       d fdd ZZ S)! MixtralConfiga This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an Mixtral 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 Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1. [mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B) [mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1) 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 Mixtral model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MixtralModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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 `8`. head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`): The attention head dimension. 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*32`): The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention allows sequence of up to 4096*32 tokens. 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*): The id of the padding token. 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. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `4096`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_experts_per_tok (`int`, *optional*, defaults to 2): The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter num_local_experts (`int`, *optional*, defaults to 8): Number of experts per Sparse MLP layer. 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. See [here]() for more details router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. router_jitter_noise (`float`, *optional*, defaults to 0.0): Amount of noise to add to the router. ```python >>> from transformers import MixtralModel, MixtralConfig >>> # Initializing a Mixtral 7B style configuration >>> configuration = MixtralConfig() >>> # Initializing a model from the Mixtral 7B style configuration >>> model = MixtralModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZmixtralZpast_key_valuesZcolwiseZrowwiseZ colwise_rep)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.block_sparse_moe.gatez&layers.*.block_sparse_moe.experts.*.w1z&layers.*.block_sparse_moe.experts.*.w2z&layers.*.block_sparse_moe.experts.*.w3Z input_idsZ inputs_embedsZ hidden_statesZattention_mask)Z embed_tokensZlayersZnorm}8 Nsilu{Gz?h㈵>TF.AMbP?c s||_| |_||_||_||_||_||_|dur|}||_||_| |_ | |_ | |_ ||_ ||_ ||_||_||_||_||_||_tjd| |||d|dS)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headssliding_windownum_key_value_heads hidden_actinitializer_range rms_norm_eps use_cache rope_thetaattention_dropouthead_dimnum_experts_per_toknum_local_expertsoutput_router_logitsrouter_aux_loss_coefrouter_jitter_noisesuper__init__)selfrrrrrrr&r rr!r"r#rrrrr$rr%r'r(r)r*r+kwargs __class__r`/var/www/auris/lib/python3.10/site-packages/transformers/models/mixtral/configuration_mixtral.pyr-s< zMixtralConfig.__init__)rrrrrr Nr r r r TNrrFrNrrr Frr) __name__ __module__ __qualname____doc__Z model_typeZkeys_to_ignore_at_inferenceZbase_model_tp_planZbase_model_pp_planr- __classcell__rrr0r2rsTU  rN) r6Zconfiguration_utilsrutilsrZ get_loggerr3loggerr__all__rrrr2s    (