ó <±hƒãóø•SSKJr SSKrSSKJr S\RS\S\R4SjrSS\RS \RS \RS \RS \\RS \S\4Sjjr g)é)ÚOptionalN)ÚnnÚ hidden_statesÚn_repÚreturncó •URup#pEUS:XaU$USS2SS2SSS2SS24RX#XU5nURX#U-XE5$)zÈ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) éN)ÚshapeÚexpandÚreshape)rrÚbatchÚnum_key_value_headsÚslenÚhead_dims Ú]/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/integrations/eager_paged.pyÚ repeat_kvrs_€ð 2?×1DÑ1DÑ.€E Ø ƒzØÐØ!¢!¢Q¨ªa²Ð"2Ñ3×:Ñ:¸5ÐW\ÐdlÓm€MØ × Ñ  ¸eÑ(CÀTÓ TÐTóÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutc óZ•URSS5nUb UR"X#UR40UD6up#[X R5n [X0R5n [ R "XRSS55U-n Ub"USS2SS2SS2SU RS24n X¼-n [RRU S[ RS9RUR5n [ R "Xº5n U RSS5R5n XÛ4$)NÚcacheéééþÿÿÿéÿÿÿÿ)ÚdimÚdtyper )ÚpopÚupdateÚ layer_idxrÚnum_key_value_groupsÚtorchÚmatmulÚ transposer rÚ functionalÚsoftmaxÚfloat32Útor"Ú contiguous)rrrrrrrÚkwargsrÚ key_statesÚ value_statesÚ attn_weightsÚ causal_maskÚ attn_outputs rÚeager_paged_attention_forwardr5s €ð J‰Jw Ó %€EØ ÑØ—\’\ #¨f×.>Ñ.>ÑIÀ&ÑI‰ ˆä˜3× ;Ñ ;Ó<€JܘU×$?Ñ$?Ó@€Lä—<’< ×';Ñ';¸A¸qÓ'AÓBÀWÑL€LØÑ!Ø$¢Qªª1Ð.D° ×0@Ñ0@ÀÑ0DÐ.DÐ%DÑEˆ Ø#Ñ1ˆ ä—=‘=×(Ñ(¨¸2ÄUÇ]Á]Ð(ÐS×VÑVÐW\×WbÑWbÓc€LÜ—,’,˜|Ó:€KØ×'Ñ'¨¨1Ó-×8Ñ8Ó:€Kà Ð $Ð$r)g) Útypingrr'rÚTensorÚintrÚModuleÚfloatr5©rrÚr<s™ðÝã Ýð U˜UŸ\™\ð U°#ð U¸%¿,¹,ô Uð&ñ%Ø I‰Ið%à <‰<ð%ð ‰ð%ð <‰<ð %ð ˜UŸ\™\Ñ*ð %ð ð %ðö%r