ip ldZddlmZddlmZddlmZejeZ GddeZ dgZ y)zPhi model configuration)PreTrainedConfig)RopeParameters)loggingc*reZdZdZdZdgZdddddddZdgdgfdgdgfd d gd gfd gd gfd Z d"d ed zded zded zded zded zded zde d zde d zde d zde d zded zde d zded zde d zde d zde e e e fzd zde d zded zded zd ed zf(fd! ZxZS)# PhiConfiga This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi 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 Phi [microsoft/phi-1](https://huggingface.co/microsoft/phi-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 51200): Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PhiModel`]. hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 8192): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. resid_pdrop (`float`, *optional*, defaults to 0.0): Dropout probability for mlp outputs. embd_pdrop (`int`, *optional*, defaults to 0.0): The dropout ratio for the embeddings. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after computing the attention scores. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_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`. Whether to tie weight embeddings or not. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings 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`. qk_layernorm (`bool`, *optional*, defaults to `False`): Whether or not to normalize the Queries and Keys after projecting the hidden states. bos_token_id (`int`, *optional*, defaults to 1): Denotes beginning of sequences token id. eos_token_id (`int`, *optional*, defaults to 2): Denotes end of sequences token id. pad_token_id (`int`, *optional*): The id of the padding token. Example: ```python >>> from transformers import PhiModel, PhiConfig >>> # Initializing a Phi-1 style configuration >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1") >>> # Initializing a model from the configuration >>> model = PhiModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```phipast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.densezlayers.*.mlp.fc1zlayers.*.mlp.fc2 input_ids inputs_embeds hidden_statesattention_mask) embed_tokens embed_dropoutlayersfinal_layernormN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads resid_pdrop embd_pdropattention_dropout hidden_actmax_position_embeddingsinitializer_rangelayer_norm_eps use_cachetie_word_embeddingsrope_parameters qk_layernorm bos_token_id eos_token_id pad_token_idc h||_||_||_||_||_||}||_||_||_| |_| |_ | |_ | |_ | |_ ||_ ||_||_|j!dd||_||_||_||_t+|Xdi|y)Npartial_rotary_factorg?)rrrrrrrrrrrrr r!r$r# setdefaultr%r&r'r"super__init__)selfrrrrrrrrrrrrr r!r"r#r$r%r&r'kwargs __class__s e/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/phi/configuration_phi.pyr-zPhiConfig.__init__xs0%&!2!2#6  &"5 #6 &$!2$'>$!2,"(.137(((#6  "6")ii  Nr6r6gelu_newr3g{Gz?gh㈵>TFNFN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintfloatstrboolrdictr- __classcell__)r0s@r1rrsK ZJ#4"5%.%.%.$-%% &(9:*+o->?#%568IJ,-/@A "'"&(,(**,*.$'#&*-!+.2*.%)!%+0MQ$)#$#$#'+2#$J2#4Z2#: 2# : 2# !4Z 2#!4Z2#T\2#DL2#!4<2#$J2#"%t2#!4<2#d 2#$;2# "D[!2#"($sN/B*CCdJ#2#$Tk%2#&Dj'2#(Dj)2#*Dj+2#2#r2rN) r=configuration_utilsrmodeling_rope_utilsrutilsr get_loggerr:loggerr__all__r*r2r1rNs@31   H %Q# Q#h -r2