.i)ddlZddlmZddlmZddlmZddlmZm Z m Z m Z m Z Gdd eZ Gd d e ZGd d e ZGddeZGdde ZGdde ZgdZy)N)Cache)RopeParameters) Gemma2Config)Gemma2AttentionGemma2DecoderLayerGemma2ForCausalLM Gemma2MLP Gemma2RMSNormc2\eZdZdZ ddedzdedzdedzdedzdedzdedzd edzd edzd edzd edzd edzdedzdedzdedzdedzdedzdee eefzdzdedzdedzdedzdedzde edzdedzdedzf0fd Z xZ S)VaultGemmaConfiga This is the configuration class to store the configuration of a [`VaultGemmaModel`]. It is used to instantiate an VaultGemma 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 VaultGemma-7B. e.g. [google/vaultgemma-7b](https://huggingface.co/google/vaultgemma-7b) 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 VaultGemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`VaultGemmaModel`] hidden_size (`int`, *optional*, defaults to 2304): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 9216): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 26): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. 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-06): 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*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): 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`. attention_bias (`bool`, defaults to `False`, *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. query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in VaultGemma, every other layer uses sliding window attention. This is the size of the sliding window. layer_types (`list`, *optional*): Attention pattern for each layer. final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores. ```python >>> from transformers import VaultGemmaModel, VaultGemmaConfig >>> # Initializing a VaultGemma vaultgemma-7b style configuration >>> configuration = VaultGemmaConfig() >>> # Initializing a model from the vaultgemma-7b style configuration >>> model = VaultGemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```N vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_headshead_dimhidden_activationmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id eos_token_id bos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutquery_pre_attn_scalarsliding_window layer_typesfinal_logit_softcappingattn_logit_softcappingc t|did|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d|d|d|d|d|d|||`y)Nrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&)super__init__use_bidirectional_attention)selfrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&kwargs __class__s m/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/vaultgemma/modular_vaultgemma.pyr*zVaultGemmaConfig.__init__hs 8  ! # 0 0  !4  !4   0 %< 0 &   & & & !4! ",# $*% &0' (#8) **+ ,$- .%TrrTNFgr4iNg>@gI@) __name__ __module__ __qualname____doc__intstrfloatboolrdictlistr* __classcell__r.s@r/rrsM b"("&(,(**+*+"(;.2*.#'!%#$#$#$+/MQ&+*-,/%)(,04/338-$J8-4Z8-: 8- : 8- !4Z 8-!4Z8-*8-:8-"%t8-!4<8-Dj8-$;8-Dj8-Dj8- Dj!8-""D[#8-$($sN/B*CCdJ%8-&t '8-(!4<)8-* #Tz+8-,d -8-.#Y%/8-0"'18-2!& 38-8-r0rc eZdZy)VaultGemmaRMSNormNr7r8r9r(r0r/rDrDr0rDc eZdZy) VaultGemmaMLPNrEr(r0r/rHrHrFr0rHc,eZdZdZdedeffd ZxZS)VaultGemmaAttentionz=Multi-headed attention from 'Attention Is All You Need' paperconfig layer_idxc0t|d|_y)NT)r)r* is_causal)r,rKrLr.s r/r*zVaultGemmaAttention.__init__s r0)r7r8r9r:rr;r*rArBs@r/rJrJsG/Cr0rJc<eZdZfdZ d dej deej ej fdej dzdejdzdedzdejdzd eejeejejfdzffd Z xZ S) VaultGemmaDecoderLayerc ,t|di||`|`y)Nr()r)r*post_attention_layernormpost_feedforward_layernorm)r, super_kwargsr.s r/r*zVaultGemmaDecoderLayer.__init__s (<(  )  +r0N hidden_statesposition_embeddingsattention_mask position_idspast_key_valuescache_positionreturnc |}|j|}|jd||||||d|\}} ||z}|}|j|}|j|}||z}|S)N)rUrVrWrXrYrZr()input_layernorm self_attnpre_feedforward_layernormmlp) r,rUrVrWrXrYrZr-residual_s r/forwardzVaultGemmaDecoderLayer.forwards!,,]; )4>> ' 3)%+)    q!=0  66}E /  =0 r0)NNNN) r7r8r9r*torchTensortuple LongTensorr FloatTensorrcrArBs@r/rPrPs,/304(,26||#5<<#=> t+  &&-   ((4/ u  %(9(95;L;L(L"MPT"TT Ur0rPc eZdZy)VaultGemmaForCausalLMNrEr(r0r/rjrjrFr0rj)rrjVaultGemmaModelVaultGemmaPreTrainedModel)rd cache_utilsrmodeling_rope_utilsrgemma2.configuration_gemma2rgemma2.modeling_gemma2rr r r r rrDrHrJrPrj__all__r(r0r/rrsn  16uuH-|H-V   I /#/#L -  r0