i`ddlmZddlmZddlZddlmZddlmZddlm Z m Z ddl m Z dd l mZmZdd lmZmZdd lmZmZmZmZdd lmZmZdd lmZmZddlmZm Z ddl!m"Z"ddl#m$Z$m%Z%m&Z&ddl'm(Z(m)Z)ddl*m+Z+ddl,m-Z-edGddej\Z/Gddej\Z0dZ1edd=dZ2dejfde4dejffd Z5 d>d!ej\d"ejfd#ejfd$ejfd%ejfdzd&e6d'e6d(e"e$fd)Z7Gd*d+ej\Z8Gd,d-ej\Z9Gd.d/eZ:e%Gd0d1e Z;e%Gd2d3e;ZGd8d9ee;Z?Gd:d;ee;Z@gd<ZAy)?)Callable)OptionalN)nn)ACT2FN)Cache DynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hub)create_causal_mask!create_sliding_window_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs) Exaone4ConfigRMSNormcheZdZddeddffd Zdej dej fdZdZxZ S) Exaone4RMSNormepsreturnNct|tjt j ||_||_y)z= Exaone4RMSNorm is equivalent to T5LayerNorm N)super__init__r Parametertorchonesweightvariance_epsilon)self hidden_sizer% __class__s h/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/exaone4/modeling_exaone4.pyr)zExaone4RMSNorm.__init__3s1 ll5::k#:; # hidden_statesc"|j}|jtj}|j dj dd}|tj ||jzz}|j|j|zS)NT)keepdim) dtypetor+float32powmeanrsqrtr.r-)r/r4 input_dtypevariances r2forwardzExaone4RMSNorm.forward;sy#)) %((7  $$Q',,R,>% Ht?T?T4T(UU {{]--k:::r3c^t|jjd|jS)Nz, eps=)tupler-shaper.)r/s r2 extra_reprzExaone4RMSNorm.extra_reprBs* ))*+6$2G2G1HIIr3)gư>) __name__ __module__ __qualname__floatr)r+TensorrArE __classcell__r1s@r2r$r$1s7$$$$;U\\;ell;Jr3r$c eZdZUejed<d deffd Ze d dedzde dde dzde d e ffd Z ejed ZxZS)Exaone4RotaryEmbeddinginv_freqNconfigct||j|_|j|_||_|j j d|_|j}|jdk7rt|j}||j |\}|_ |jd|d|jd|jdy)N rope_typedefaultrOF) persistentoriginal_inv_freq) r(r)max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrPrope_parametersrRcompute_default_rope_parametersrattention_scalingregister_bufferclone)r/rPdevice rope_init_fnrOr1s r2r)zExaone4RotaryEmbedding.__init__Is "("@"@$*$B$B! 44[A!%!E!E >>Y &.t~~>L+7 V+L($( ZeD 0(..2BuUr3r^z torch.deviceseq_lenr&z torch.Tensorc|jd}t|ddxs|j|jz}d}d|t j d|dtj j|tj|z zz }||fS) a Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). rope_thetahead_dimNg?rr6r9)r^r9) rYgetattrr0num_attention_headsr+arangeint64r:rI)rPr^r`basedimattention_factorrOs r2rZz6Exaone4RotaryEmbedding.compute_default_rope_parametersYs&%%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjj k )))r3cN|jddddfjj|jdddj |j }|dddddfj}t |j jtr/|j jdk7r|j jnd}t|d5|j|jzjdd}tj||fd }|j|jz}|j|jz} dddj |j  j |j fS#1swYAxYw) Nrr7r mpscpuF) device_typeenabledr6rjrd)rOrIexpandrDr:r^ isinstancetypestrr transposer+catcosr[sinr9) r/x position_idsinv_freq_expandedposition_ids_expandedrofreqsembrxrys r2rAzExaone4RotaryEmbedding.forwardwsR!MM$4-8>>@GG HZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk  U C 5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C  5 vvAGGv$cff177f&;;;  5 5s BFF$NNNN)rFrGrHr+rJ__annotations__r!r) staticmethodrintrCrIrZno_gradrrArKrLs@r2rNrNFsllV}V '++/"*$*(*t* ~u$ % **:U]]_ <