o ¾e¦iÍ ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zStarcoder2 model configurationé)ÚPreTrainedConfig)ÚRopeParameters)Úloggingc,sDeZdZdZdZdgZdddddddœZdgdgfd d gd gfd gd gfd œZ                   d1dedBdedBdedBdedBdedBdedBd e dBd!edBd"e dBd#edBd$e dBd%edBd&edBd'edBd(e e e e fBdBd)edBd*e dBd+e dBd,e dBd-e dBd.e dBf*‡fd/d0„ Z‡ZS)2ÚStarcoder2Configa“ 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 ```Ú starcoder2Úpast_key_valuesÚcolwiseÚrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.c_fczlayers.*.mlp.c_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnorméÀé é0éééÚgelu_pytorch_tanhéçŸVÑšy’?çñh㈵øä>TéPÄNçÚ vocab_sizeÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚ hidden_actÚmax_position_embeddingsÚinitializer_rangeÚ norm_epsilonÚ use_cacheÚ bos_token_idÚ eos_token_idÚ pad_token_idÚrope_parametersÚsliding_windowÚattention_dropoutÚresidual_dropoutÚembedding_dropoutÚuse_biasÚtie_word_embeddingsc s”||_||_||_||_||_||_||_||_||_||_ | |_ | |_ | |_ ||_ ||_||_||_| |_| |_||_||_tƒjdi|¤ŽdS)N©)rr$rrr r!r,r0r"r#r%r&r'r-r.r/r+r(r)r*r1ÚsuperÚ__init__)Úselfrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1Úkwargs©Ú __class__r2úu/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/starcoder2/configuration_starcoder2.pyr4xs,zStarcoder2Config.__init__)rrrrrrrrrrTrrNNNrrrTT)Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planÚintÚstrÚfloatÚboolrÚdictr4Ú __classcell__r2r2r7r9rsžNú  ýêþýüûúùø ÷ ö õ ô óòñðïîíìëêrN) r=Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr:ÚloggerrÚ__all__r2r2r2r9Ús