.`i%&^dZddlmZddlmZejeZGddeZdS)zNemotron model configuration)PretrainedConfig)loggingc\eZdZdZdZdgZ dfd ZdZxZS)NemotronConfiga This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate a Nemotron 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 Nemotron-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 256000): Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`NemotronModel`] hidden_size (`int`, *optional*, defaults to 6144): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): 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 48): Number of attention heads for each attention layer in the Transformer decoder. head_dim (`int`, *optional*): Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if None 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 `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.0134): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the 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 2): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 3): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_parameters (`dict`, *optional*): The parameters of the RoPE embeddings. Expected contents: `rope_theta` (`float`): The base period of the RoPE embeddings. `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `partial_rotary_factor` (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding. 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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj and down_proj layers in the MLP layers. ```python >>> from transformers import NemotronModel, NemotronConfig >>> # Initializing a Nemotron nemotron-15b style configuration >>> configuration = NemotronConfig() >>> # Initializing a model from the nemotron-15b style configuration >>> model = NemotronModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```nemotronpast_key_values` 0Nrelu2S!uq?h㈵>TFc ||_| |_||_||_||_||_|p|d}||n||z|_||}||_||_ | |_ | |_ | |_ | dd}|p|pddi}| dd}d|vr||d<|dp+|dp|d pd }d |vr||d <||_|||_||_||_t'jd | |||d |dS) N kv_channels rope_scaling rope_typedefault rope_thetag@ rope_percentrope_percentagepartial_rotary_factorg?) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsgethead_dimnum_key_value_heads hidden_actinitializer_rangenorm_eps use_cachepoprope_parameters_rope_parameters_validationattention_biasattention_dropoutmlp_biassuper__init__)selfr#r%r&r'r(r*r+r,r$r-r.r/rrr r!r1r3r4r5kwargsrrr __class__s |/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/vllm/transformers_utils/configs/nemotron.pyr7zNemotronConfig.__init__zs0%'>$&!2!2#6 8vzz-88 ,HH;BU3U  &"5 #6 $!2  "zz.$77 &U/Uk9=UZZ g66  . .,6OL ) JJ~ & & zz+,, zz122   #/ 9 97LO3 4. ((***,!2   %%% 3         c<|jdS|jdd}|jdd}|dvrtd||dkr>|tdt|tr|dkrtd |dSdS) z? Validate the `rope_parameters` configuration. Nrfactor>linearrdynamiczA`rope_type` must be one of ['default', 'linear', 'dynamic'], got rz]If `rope_type` is not 'default', `rope_parameters` must include a `factor` field. Got `None`.g?z:`rope_parameters`'s factor field must be a float > 1, got )r1r) ValueError isinstancefloat)r8rr>s r;r2z*NemotronConfig._rope_parameters_validations   ' F $ 4 8 8d K K #377$GG < < <# ##   ! !~ Afe,, #      " ! 1> r<)r r r r r NNrrrrTNrrFNFrF) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer7r2 __classcell__)r:s@r;rrsZ Z xJ#4"5  $ !+G G G G G G Rr<rN) rG transformersrtransformers.utilsr get_loggerrDloggerrr"r<r;rOs$#"))))))&&&&&&  H % %BBBBB%BBBBBr<