*i ldZddlmZddlmZddlmZejeZ GddeZ dgZ y)zStarcoder2 model configuration)PreTrainedConfig)RopeParameters)loggingc,teZdZdZdZdgZdddddddZdgdgfd d gd gfd gd gfd Z d#d ed zded zded zded zded zded zde d zded zde d zded zde d zded zded zded zde e e e fzd zded zde d zde d zde d zd e d zd!e d zf*fd" ZxZS)$Starcoder2Configa This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a Starcoder2 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 [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b) model. 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 49152): Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Starcoder2Model`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2): 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 `8`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): 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. Starcoder2'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. norm_epsilon (`float`, *optional*, defaults to 1e-05): Epsilon value for the layer norm 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 50256): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 50256): The id of the "end-of-sequence" token. pad_token_id (`int`, *optional*): Padding token id. 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`. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `None` (no sliding window). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. residual_dropout (`float`, *optional*, defaults to 0.0): Residual connection dropout value. embedding_dropout (`float`, *optional*, defaults to 0.0): Embedding dropout. use_bias (`bool`, *optional*, defaults to `True`): Whether to use bias term on linear layers of the model. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings ```python >>> from transformers import Starcoder2Model, Starcoder2Config >>> # Initializing a Starcoder2 7B style configuration >>> configuration = Starcoder2Config() >>> # Initializing a model from the Starcoder2 7B style configuration >>> model = Starcoder2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` starcoder2past_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.c_fczlayers.*.mlp.c_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range norm_epsilon use_cache bos_token_id eos_token_id pad_token_idrope_parameterssliding_windowattention_dropoutresidual_dropoutembedding_dropoutuse_biastie_word_embeddingsc J||_||_||_||_||_||_||_||_||_||_ | |_ | |_ | |_ ||_ ||_||_||_| |_| |_||_||_t+|Xdi|y)N)rrrrrrr"r&rrrrrr#r$r%r!rrr r'super__init__)selfrrrrrrrrrrrrrr r!r"r#r$r%r&r'kwargs __class__s s/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/starcoder2/configuration_starcoder2.pyr+zStarcoder2Config.__init__xs2%'>$&!2!2#6 ,  #6 $!2("!2 0!2.(((#6  "6")ii i0gelu_pytorch_tanhigVy?gh㈵>TPr5NNNr6r6TT)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictr+ __classcell__)r.s@r/rrsL \J#4"5&/%.%.%.&( &(9:#%568IJ!"_$56"'"&(-(**,*+!4.2*2#'!%#(#(#'MQ%)*-),*- $+/-/#$J/#4Z/#: /# : /# !4Z /#!4Z/#$J/#"%t/#!4</#Dj/#$;/#Dj/#Dj/#Dj/# ($sN/B*CCdJ!/#"d #/#$!4<%/#& $,'/#(!4<)/#*++/#,"D[-/#/#r0rN) r:configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr7loggerr__all__r)r0r/rKsA%31   H %O#'O#d  r0