UhY5ddlZddlmZddlmZmZmZmZmZddl Z ddl m Z ddl m cm ZddlmZddlmZddlmZmZmZddlmZdd lmZdd lmZdd lmZdd lm Z m!Z!m"Z"m#Z#dd l$m%Z%m&Z&ddl'm(Z(m)Z)ddl*m+Z+ddl,m-Z-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4e0r ddl5m6Z6ddl7m8Z8e1jre:Z;Gdde jxZ=Gdde jxZ>Gdde jjxZ?Gdde jxZ@edGdde jxZAGdd e jxZBd!e jd"e jd#e jd$ee je jffd%ZDd&e jd'eEd$e jfd(ZF d[d)e jxd*e jd+e jd,e jd-ee jd.eGd/eGfd0ZH d[d)e jxd*e jd+e jd,e jd-ee jd.eGd/eGfd1ZIGd2d3e jxZJGd4d5e jxZKe.Gd6d7e)ZLe.Gd8d9eLZMGd:d;ee-ZNGd<d=eLeZOeGd>d?e#ZPGd@dAe jjxZQGdBdCe jxZRdDZSGdEdFe jxZTdGe jd*e jfdHZUd*e jd+e jdGe jd$ee je jffdIZVGdJdKe jxZWGdLdMe jxZXGdNdOe jxZYGdPdQe jxZZGdRdSe jxZ[GdTdUe jxZ\GdVdWeLZ]GdXdYeLeZ^gdZZ_y)\N) dataclass)CallableListOptionalTupleUnion)Llama4VisionConfig)ACT2FN)Cache DynamicCacheHybridChunkedCache)GenerationMixin)use_kernel_forward_from_hub)AttentionMaskConverter)FlashAttentionKwargs)BaseModelOutputBaseModelOutputWithPastCausalLMOutputWithPast ModelOutput)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack) LossKwargsauto_docstringcan_return_tupleis_torch_flex_attn_availablelogging) Llama4ConfigLlama4TextConfig) BlockMask)make_flex_block_causal_maskc\eZdZdeffd Zdej dej fdZxZS)Llama4TextExpertsconfigct||j|_|j|_|j |_|j|_tjtj|j|j d|j z|_ tjtj|j|j |j f|_ t|j|_yN)super__init__num_local_experts num_expertsintermediate_size hidden_size expert_dimnn Parametertorchempty gate_up_proj down_projr hidden_actact_fnselfr( __class__s |/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/llama4/modeling_llama4.pyr-zLlama4TextExperts.__init__1s !33!'!9!9!--00LLT5E5EtGWGWYZ]a]l]lYl)mnekk43C3CT__VZVfVf2g&hiV../ hidden_statesreturnc\|j|jd|j}tj||j }|j dd\}}tj||j|z|j}|jd|j}|S)a2 This should really not be run on a single machine, as we are reaching compute bound: - the inputs are expected to be "sorted" per expert already. - the weights are viewed with another dim, to match num_expert, 1, shape * num_tokens, shape Args: hidden_states (torch.Tensor): (batch_size * token_num, hidden_size) selected_experts (torch.Tensor): (batch_size * token_num, top_k) routing_weights (torch.Tensor): (batch_size * token_num, top_k) Returns: torch.Tensor r+dim) viewr/r1r5bmmr7chunkr:r8)r<r@gate_upgateup next_statess r>forwardzLlama4TextExperts.forward;s&**4+;+;RAQAQR ))M4+<+<====+biidkk$&7!7$..I !&&r4+;+;< r?) __name__ __module__ __qualname__r#r-r5TensorrM __classcell__r=s@r>r'r'0s+0/0U\\ellr?r'c&eZdZdfd ZdZxZS) Llama4TextMLPcft|| |j}||_t j |j |d|_t j |j |d|_t j ||j d|_ t|j|_ yNFbias) r,r-r0r(r3Linearr1 gate_projup_projr8r r9 activation_fn)r<r(r0r=s r>r-zLlama4TextMLP.__init__Rs   $ & 8 8  6#5#57HuUyy!3!35FUS #4f6H6HuU#F$5$56r?c|j|j||j|z}|j|SN)r]r[r\r8)r<xr8s r>rMzLlama4TextMLP.forward^s7&&t~~a'89DLLOK ~~i((r?r_rNrOrPr-rMrRrSs@r>rUrUQs  7)r?rUc8eZdZddeffd ZdZdZdZxZS)Llama4TextL2Normepsc0t|||_yr_)r,r-rd)r<rdr=s r>r-zLlama4TextL2Norm.__init__ds r?c|tj|jdjdd|jzzSNr+rCT)keepdimr5rsqrtpowmeanrdr<r`s r>_normzLlama4TextL2Norm._normh45;;quuQx}}R}>IJJJr?c^|j|jj|Sr_)rnfloattype_asrms r>rMzLlama4TextL2Norm.forwardks"zz!'')$,,Q//r?c d|jS)Nzeps=rdr<s r> extra_reprzLlama4TextL2Norm.extra_reprnsdhhZ  r?)gư>) rNrOrPrqr-rnrMrvrRrSs@r>rcrccsEK0!r?rcc2eZdZdfd ZdZdZdZxZS)Llama4TextRMSNormct|||_tjt j ||_y)z< Llama4RMSNorm is equivalent to T5LayerNorm N)r,r-rdr3r4r5onesweight)r<r1rdr=s r>r-zLlama4TextRMSNorm.__init__ss0 ll5::k#:; r?c|tj|jdjdd|jzzSrgrirms r>rnzLlama4TextRMSNorm._norm{ror?c||j|jj|}||jzSr_)rnrqrrr{)r<r`outputs r>rMzLlama4TextRMSNorm.forward~s0AGGI&..q1 ##r?c^t|jjd|jS)Nz, eps=)tupler{shaperdrus r>rvzLlama4TextRMSNorm.extra_reprs' ))*+6$((<)rNrOrPr-rnrMrvrRrSs@r>rxrxrs<K$=r?rx Llama4TextMoec$eZdZfdZdZxZS)rc*t||j|_|j|_|j |_t||_ tj|j|j d|_ t||_yrW)r,r-num_experts_per_toktop_kr1 hidden_dimr.r/r'expertsr3rZrouterrU shared_expertr;s r>r-zLlama4TextMoe.__init__sp //  ,,!33(0 ii 2 2F4L4LSXY *62r?c|j\}}}|jd|j}|j|}||z}t j ||j d\}}t j|tdjd||jdd} t j||jjddj| jdd}t j | jj#|j$} |jddjd|}t j&|d|j#|j} | | jddz} |j)| } |j+|} | j-d|| jd|| | fS) NrCr!rDz-infrdevice)inputrEindex)rErsrc)rreshaperrr5topkr full_likerqscatter_ transposearangerrFexpandsizesigmoidtodtypegatherrr scatter_add_) r<r@batchseq_lenr router_logitstokens_per_expertrouter_top_valuerouter_indices router_scores routed_in routed_outouts r>rMzLlama4TextMoe.forwards%2%8%8"w %--b$//B  M2 !GO+0::mTZZUV+W(. OOM5= 9 B B1nVf g q qrsuv w  LL*=3G3G H M MaQS T [ [\i\n\nop\qsu v  m&9&9&;<?? @S@ST '//A6==b*MLL   "] ! ! "   5 5b! << \\),   / Qn*//"j:YZM!!r?rarSs@r>rrs 3"r?c^eZdZddeffd Zej edZxZ S)Llama4TextRotaryEmbeddingr(c`t||jdnd|_|j|_|j|_||_t|j|_ |j|j|\}|_ |jd|d|j|_ y)Nllama3defaultinv_freqF) persistent)r,r- rope_scaling rope_typemax_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr(r rope_init_fnattention_scalingregister_bufferroriginal_inv_freq)r<r(rrr=s r>r-z"Llama4TextRotaryEmbedding.__init__s %+%8%8%D)"("@"@$*$B$B! /?+/+<+rMz!Llama4TextRotaryEmbedding.forwards !MM$4-8>>@GG HZHZ[\H]_acde ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk ^^ U C ;&))!((36KKVVWXZ[\E EOOE$:EBI!D$:$::I ;   ; s A'D88Er_) rNrOrPr#r-r5no_gradrrMrRrSs@r>rrs4 // /U]]_  r?rxqxkrrAc &tj|jjg|jdddd}tj|jjg|jdddd}tj ||dddddddfzj d}tj ||dddddddfzj d}|j||j|fS)NrCr+r )r5view_as_complexrqrr view_as_realflattenrr)rrrxq_xk_xq_outxk_outs r>apply_rotary_embrs    2 2 2 IBHHSbM I2 Iq I JC    2 2 2 IBHHSbM I2 Iq I JC   i1dA &> > ? G G JF   i1dA &> > ? G G JF >>" v~~b1 11r?r@n_repc|j\}}}}|dk(r|S|dddddddddfj|||||}|j|||z||S)z 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) r!N)rrr)r@rrnum_key_value_headsslenhead_dims r> repeat_kvrso 2?1D1D.E h z!!Qa"23::5BUW\^bdlmM  (;e(CT8 TTr?modulequerykeyvalueattention_maskscalingdropoutc t||j}t||j} tj||j dd|z} |#|ddddddd|j df} | | z} t jj| d} t jj| ||j} tj| | } | j ddj} | | fS)Nr+r rCrDptrainingr!) rnum_key_value_groupsr5matmulrrr3 functionalsoftmaxrr contiguous rrrrrrrkwargs key_states value_states attn_weights causal_mask attn_outputs r>eager_attention_forwardrs3 ; ;L==((6??([L,,|\:K''1-88:K  $$r?c t||j}t||j} tj||j dd|j dzz} |#|ddddddd|j df} | | z} tjj| d} tjj| ||j} tj| | } | j ddj} | | fS) Nr+r rrCrDrr!) rrr5rrrrr3rrrrrrs r>vision_eager_attention_forwardrs3 ; ;L==((6??([L,,|\:K''1-88:K  $$r?c2eZdZdZdeffd Z d dejdeejejfde ejde e de ejd e e d eeje eje eejffd ZxZS) Llama4TextAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr(ct|||_||_t |d|j |j z|_|j |_|j |jz|_ |j|_|jdz|_ |j|_ |j|_ |j|_ |j|_d|_|j ||_t%j&|j |j |jz|j(|_t%j&|j |j|jz|j(|_t%j&|j |j|jz|j(|_t%j&|j |jz|j |j(|_|jj2r(|j"rt5|j6|_yyy)NrrTrX)r,r-r( layer_idxgetattrr1num_attention_headsrrrr attn_scale floor_scaleattn_temperature_tuningattention_dropout is_causalno_rope_layersuse_roper3rZattention_biasq_projk_projv_projo_proj use_qk_normrc rms_norm_epsqk_normr<r(rr=s r>r-zLlama4TextAttention.__init__#s  " F4F4F&JdJd4de #)#=#= $*$>$>&B\B\$\!#)#=#= }}d*  ++!--'-'E'E$!'!9!9--i8 ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii  & & 68J8JQWQfQf   ;; " "t}}+F,?,?@DL(5 "r?r@position_embeddingsrpast_key_valuecache_positionrrAc |jdd}g|d|j}|j|j|} |j |jg|d|j} |j |j|j dd} |jr)t| | |j| j\} } t|dr"|j| } |j| } |jr|jstjtj |j#dz|j$z dz|j&zdz} | jd|dddfj)g|dd} | | zj| j*} | j dd} | j dd} |%d|i} |j-| | |j.| \} } t0}|j2j4dk7r^|j2j4dk(r(|j7d d rt8j;d nt<|j2j4}||| | | |f|j>sd n |j@|jBd |\}}|jDg|djG}|jI|}||fS)NrCr!r+r ?reagersdpaoutput_attentionsF`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.)rr)%rrrrFrrrrrrrhasattrr rr5logfloorrqrrrrupdaterrr(_attn_implementationgetlogger warning_oncerrrrrrr)r<r@r rr rr input_shape hidden_shape query_statesrr attn_scales cache_kwargsattention_interfacerrs r>rMzLlama4TextAttention.forwardAs$))#2. 88b8$--8 {{=166|D 4T[[/44UkU2Ut}}U {{=166|DNNqRST =='7j*=*@*@ATAT*U( $L* 4 #<< 5Lj1J  ' '  %++~';';'='CtGWGW&WX[^^_bfbqbqqtww &**A{21+EFMMNbP[Nb]^Nb`aNbcK(;6::<;M;MNL#--a3 ))!Q/  %,n=L'5'<'d##L '>dkk>^>^&_#$7      % $}}C$2H2HLL %  % ! \*k));;;;FFH kk+. L((r?NN)rNrOrP__doc__r#r-r5rQrrr LongTensorrrrMrRrSs@r>rr sGA/AF+/59 ?)||?)#5<<#=>?)!. ?) ! ?) !!1!12 ?)-.?) u||Xell3XeELL>Q5RR S?)r?rceZdZfdZ ddej deej deej deejdeeej dee dee d ee d eejd eeej ej fd e e d eejeeejejfffdZ xZS)Llama4TextDecoderLayerct||j|_t|||_|j duxrt |j||_||jv|_ |jrt||_ nt||j|_ t|j|j |_t|j|j |_||_y)N)r0rt)r,r-r1r self_attnattention_chunk_sizeboolruse_chunked_attention moe_layers is_moe_layerr feed_forwardrUintermediate_size_mlprxr input_layernormpost_attention_layernormrr s r>r-zLlama4TextDecoderLayer.__init__s !--,VY?%+%@%@%L%wQUV\VkVkluVvQw"%):)::    -f 5D  -fHdHd eD 01C1CI\I\](9&:L:LRXReRe(f%"r?r@rchunk_causal_maskrr routput_router_logits use_cacherr rrAc b|} |j|}|jr||}|jd|| ||||| d| \} }| | z}|} |j|}|j |}|j r|\}}nd}| |j | jz}|f}|r||fz }|r||fz }|S)N)r@r rr rr6r)r2r-r*r3r0r/rFr)r<r@rr4rr rr5r6rr rresidualattention_statesself_attn_weightsroutputss r>rMzLlama4TextDecoderLayer.forwards!,,];   % %*;*G.N/=dnn / ' 3))/) /  / ++!#33 !55mD ))-8   +8 (M= M =#5#5hnn#EE  "  )+ +G   ' 'Gr?) NNNNFFFNN)rNrOrPr-r5rQrr&rr,rr FloatTensorrMrRrSs@r>r(r(s.#&2648378<,1/4$)59KO5||5!.5$ELL1 5 u//0 5 !u||!45 5$D>5'tn5D>5!!1!125&eELL%,,,F&GH5-.5 u  (51B1BEDUDU1U+V"WW X5r?r(c<eZdZeZdZdgZdZdZdZ dZ dZ dZ dZ dZy)Llama4PreTrainedModelTpast_key_valuesFcVt|jdr|jjn|jjj}t |t j rY|jjjd||j%|jjjyyt |t jrf|jjjd||j2|jj|jjyyt |t jrJ|jjjd|jjjyt |t r&|jjjdyt |t"rO|j$jjd||j&jjd|yt |t(ra|j*jj|j,|j.jj|j,yy)Ninitializer_ranger)rlstdr)rC)rr(rB text_configrr3rZr{datanormal_rYzero_ Embedding padding_idx LayerNormfill_rxr'r7r8Llama4VisionModelclass_embeddingscalepositional_embedding_vlm)r<rrCs r> _init_weightsz#Llama4PreTrainedModel._init_weightsst{{$78 KK ) )((:: fbii ( MM   & &CS & 9{{&   &&('  - MM   & &CS & 9!!- ""6#5#56<<>.  - MM   $ $S ) KK   " " $  1 2 MM   $ $S )  1 2    $ $ , ,#3 , ?    ! ! ) )s ) <  1 2  " " ' ' / /FLL / A  + + 0 0 8 8V\\ 8 J3r?N)rNrOrPr" config_classsupports_gradient_checkpointing_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_flex_attn_supports_cache_class_supports_quantized_cache_supports_static_cache_supports_attention_backendrPr8r?r>r?r?sEL&*##4"5"N  $!"&Kr?r?ceZdZdgZdZeZdeffd ZdZdZ e e d"de jdee jd ee jd eed ee j"d eed eedeedeedee jdeedeeeffdZe j2j5d d#de jde jde jd ed ef dZdededede j:de jf dZede jdedede j@de jd ef d!Z!xZ"S)$Llama4TextModelr(modelr(c  t|||j|_|j|_t j |j|j|j|_t jt|jDcgc]}t||c}|_ t|j|j|_t#||_d|_|j)ycc}w)Nrt)r(F)r,r- pad_token_idrI vocab_sizer3rHr1 embed_tokens ModuleListrangenum_hidden_layersr(layersrxr normr rotary_embgradient_checkpointing post_initr s r>r-zLlama4TextModel.__init__s  !.. ++LL):):FQ>QR 36B&+#  isDc|jSr_rarus r>get_input_embeddingsz$Llama4TextModel.get_input_embeddings s   r?c||_yr_rkr<rs r>set_input_embeddingsz$Llama4TextModel.set_input_embeddings s !r? input_idsrrr@ inputs_embedsr6routput_hidden_states return_dictrflash_attn_kwargsrAc  H||n|jj}||n|jj}||n|jj}| | n|jj} |du|duz r t d|j r%|jr|rtjdd}|>|j|j|jjj}|rb|`|jjj2t!|j|j"d|j"d}n t%}| F||j'nd} t)j*| | |j"dz|j} || j-d}|j/||| |||\} }|}|j1||}|rdnd}|rdnd}|j2d|jj4D]k}|r||fz }|j r2|jr&|j7|j8|| ||||d|| | }n||f| |||||| |d | }|d}|sc||dfz }m|j;|}|r||fz }t=||r|nd|| S) N:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.Frr!r)r6r8)rr4rr rr6rr )last_hidden_stater@r@ attentions)r(rrrr6use_return_dict ValueErrorrhrrrrarr{rget_text_configr+rrr get_seq_lengthr5r unsqueeze_update_causal_maskrgrerd_gradient_checkpointing_func__call__rfr)r<rprrr@rqr6rrrrsrrtpast_seen_tokensrr4r@freq_cisall_hidden_statesall_self_attns decoder_layer layer_outputss r>rMzLlama4TextModel.forwards 2C1N-TXT_T_TqTq$8$D $++JjJj "+!6IDKK?L]ir*B* & && ??=,?#7BD0d![[)H4;;+H+HI# 6M#!m%55!**t}} $ A A!**!% #%" ! !.! !#.&7!-#2&7'#1(0 !( ! *!,M =#3"55G# 6J -0  -!1 1 &+/8Od+%   r?F) recursive input_tensorc &|jjdk(r||dk(jr||fSy|jjdvry|jd}|j |j }|jj } | du} |d} ||jxs|} n||jdn|} | rL| | k\} | | k| |z| kDz}|r5tj| | |zdz tj|| |z| n| }|jjdk(rit|tjr;| r$| t| | z dzdf}t|| || }t||| | df }||fSt|tr||fS|j|j }}| r t| | n| }|j!||||||jd }| rE| | kDr?t| | z dzd}|z}|j#|jj ||| }|dd||f}|jd| k}|r2t$j&j)|d| |jdz f}|s|dd| dddf}n |dd|ddf}|j+|jdddd}||ddddddfz}|jjd k(rHtj,|j.}tj|dk(|dj |}|jjdk(r`|^|j j0dvrF|j2dk(r7|s5tj,|j.}t5j6||}|jjdk(rY|W|j9}|tj,|j.k7}t5j:||| |j<rd}||fS)Nflash_attention_2rr$)rflex_attentionrr!rrCr)offsets) query_length key_lengthr)sequence_length target_lengthrr batch_size)startendrrr)cudaxpunpu)rqpast_key_values_length is_training)r(ranyrrrr+get_max_cache_shaper5whererrQmaxr%r$r5_prepare_4d_causal_attention_mask_with_cache_positioncreate_chunked_attention_maskr3rpadrfinfominrndimr_unmask_unattendedr,_ignore_causal_mask_sdpar)r<rrrr@rchunked_attention_maskr6rr+using_chunked_attentionfirst_cache_positionfull_cache_lengthcond1cond2rrrrrr start_idxend_idxlocal_attention_maskrequires_padding min_dtypes r>r~z#Llama4TextModel._update_causal_masks ;; + +/B B)~/D.I.I.K%~55 ;; + +3V V&,,Q/'**4;;7#{{??"6d"B-a0  & / C C E X KK';o'MOcd '  ;; + +/? ?.%,,7*3S9MPd9dgh9hjk5lmG-H&(RSTVZ\`bcSc>d%d "{{//7:!KK.22 ).5Kq5PR[]`)a)d)dej)k& KK , , 6*%%**.DD##q(%  E*..I0CCKQZ[K ;; + +v 5:P:\%;%@%@%B "%U);)?)??K%>>*'; MM  # 222r?r+rrrc(tj|||}tj|jd|z|jd|zz }|jd|jdz }|dk(|dkz}|j |S)u Generate the following: 'What' : 0 ■ ⬚ ⬚ ⬚ ⬚ ⬚ | '▁is' : 1 ■ ■ ⬚ ⬚ ⬚ ⬚ | '▁ch' : 2 ■ ■ ■ ⬚ ⬚ ⬚ | 'unked' : 3 ⬚ ⬚ ⬚ ■ ⬚ ⬚ | '▁attention': 4 ⬚ ⬚ ⬚ ■ ■ ⬚ | '?' : 5 ⬚ ⬚ ⬚ ■ ■ ■ | If the chunk size is 3. This can just be applied over the already created attention mask rrr!)r5rabsr}r) r<r+rrr arange_vector block_pos token_posmasks r>rz-Llama4TextModel.create_chunked_attention_masks  UC? II  # #A &*> >AXAXYZA[_sAs s "++A.1H1H1KK Q9>2wwvr?rrrrc ||jdk(r|}|Stj|j}tj||f|||j }|dk7rtj |d}|tj||j |jddkDz}|ddddddfj|ddd}||j}|jd} |ddddddd| f|ddddddfj|j z} | dk(} |ddddddd| fj| ||ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. Nr fill_valuerrr!diagonalrrCrrEr5rrfullrtriurrrclonerr masked_fill rrrrrrrrr mask_length padding_masks r>rzELlama4TextModel._prepare_4d_causal_attention_mask_with_cache_positions@  %.*<*<*>!*C(K*' E*..I** -0Ye\j\q\qK!##jjqA 5<< n>S>STWeWmWmnprsWtt tK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDg"))E   ,q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r?) NNNNNNNNNN)FNT)#rNrOrP_no_split_modulesbase_model_prefixr#rQr-rlrorrr5r&rrQr r=r,rrrrrrMcompilerdisabler~intrr staticmethodrrrRrSs@r>r\r\sF12#L/ !"'+1537+/59$(,0/3&*59m ##m !.m u//0 m "% m   1 12 m D>m $D>m 'tnm d^m !!1!12m $$89m  u-- .m m ^ ^^e,#(#~3 ~3ll~3 ~3  ~3  ~3-~3@$'03:=GL|| 06 666{{ 6  6  66r?r\c eZdZy)KwargsForCausalLMN)rNrOrPr8r?r>rrTsr?rc!eZdZdgZdZdgZddiZeZdeffd Z dZ d Z d Z d Z d Zd Zee ddej&deej*deej&deeeeej2fdeej2deej&deedeedeedeedeej&deeej*fdeedeeeffdZ xZ!S)Llama4ForCausalLMr(language_modelzlm_head.weightlm_head colwise_repr(ct||t||_|j|_t j |j|jd|_|jyrW) r,r-r\r]r`r3rZr1rrir;s r>r-zLlama4ForCausalLM.__init__^sU  $V,  ++yy!3!3V5F5FUS  r?c.|jjSr_r]rarus r>rlz&Llama4ForCausalLM.get_input_embeddingsgszz&&&r?c&||j_yr_rrns r>roz&Llama4ForCausalLM.set_input_embeddingsjs"' r?c|jSr_rrus r>get_output_embeddingsz'Llama4ForCausalLM.get_output_embeddingsms ||r?c||_yr_rr<new_embeddingss r>set_output_embeddingsz'Llama4ForCausalLM.set_output_embeddingsps % r?c||_yr_r]r<decoders r> set_decoderzLlama4ForCausalLM.set_decoderss  r?c|jSr_rrus r> get_decoderzLlama4ForCausalLM.get_decodervs zzr?rprrr@rqlabelsr6rrrrsrlogits_to_keeprrAc  ||n|jj}| | n|jj} | | n|jj} |jd|||||||| d| d | }|d}t | t r t| dn| }|j|dd|ddf}d}|*|jd|||jjd| }t|||j|j|jS)az labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, Llama4ForCausalLM >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```NT) rprrr@rqr6rrrrsrr)logitsrr`)lossrr@r@rxr8)r(rrrryr]rrslicer loss_functionr`rr@r@rx)r<rprrr@rqrr6rrrrsrrrr<r@ slice_indicesrrs r>rMzLlama4ForCausalLM.forwardys4P2C1N-TXT_T_TqTq$8$D $++JjJj &1%SV8W~ot4]k mA}a,?@A  %4%%pVFt{{OeOepiopD%#33!//))   r?) NNNNNNNNNNNr)"rNrOrPrr_tied_weights_keys_tp_planr#rQr-rlrorrrrrrr5r&rrQrr rr=r,rrrrrrMrRrSs@r>rrWs12(*+=)H#L/'(&'+1537KO59-1$(,0/3&*5934I ##I !.I u//0 I "%tE4E4E/F(F"GH I   1 12 I ))*I D>I $D>I 'tnI d^I !!1!12I c5<</0I *+I  u,, -I I r?rceZdZUdZdZeejed<dZ ejed<dZ ee ejed<dZ ee ejed<dZee ejed<dZeejed<y) Llama4CausalLMOutputWithPasta Base class for Llava causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. Nrrr@r@rximage_hidden_states)rNrOrPr%rrr5r=__annotations__rr@rr@rrxrr8r?r>rrs<)-D(5$$ %, $FE  $9=OXd5#4#456=8r-zLlama4VisionMLP2.__init__s !--!'!9!999T33V5O5OV[\99V88&:U:U\abWWY// r?c|j|}|j|}tj||j|j}|j|j |S)Nr)rr]Frrrr<r@s r>rMzLlama4VisionMLP2.forwardsT/ **=9  -4<<$--X !!$((="9::r?rarSs@r>rrs 0;r?rc$eZdZfdZdZxZS)Llama4MultiModalProjectorct|tj|jj |j jd|_yrW) r,r-r3rZ vision_configvision_output_dimrDr1linear_1r;s r>r-z"Llama4MultiModalProjector.__init__s?   2 2    * *  r?c(|j|}|Sr_)r)r<image_featuresr@s r>rMz!Llama4MultiModalProjector.forward s n5 r?rarSs@r>rrs  r?rc J|j\}}}ttj|}|j |||d}|j \}}}}|j ||t||zt||z }|j ddddj}|j |t||zt||zt||dzz }|j ddddj}|j |d|jd} | S)NrCrr+r!r )rrmathsqrtrFrpermuter) r shuffle_ratior num_patcheschannels patch_sizeheightwidthreshaped_tensor output_tensors r> pixel_shufflers%(4(:(:%J XTYY{+,J$$ZZLL*6*;*;*='Jx"'' FC @Ur-z$Llama4VisionPixelShuffleMLP.__init__#sX #)#=#= V77DrMz#Llama4VisionPixelShuffleMLP.forward*s#'9Q9QRxx((r?rNrOrPr-r5rQrMrRrSs@r>rr"s#,)u||) )r?rfreqs_cic|j}t|jDcgc]\}}|dk(s||dz k(r|nd}}}|j|Scc}}w)Nr!)r enumeraterrF)rrridrs r>reshape_for_broadcastr!0sW ::D=Fu{{=S TTQ!q&AMQq 0 TE T 8==%   UsAcBtj|jjg|jdddd}tj|jjg|jdddd}t ||}|j |j}tj||zjd}tj||zjd}|j||j|fS)NrCr+)rrr ) r5rrqrrr!rrrrrr)rrrquery_key_ query_outkey_outs r>vision_apply_rotary_embr'6s  " "#85;;=#8#8#R%++cr:J#RB#RPQ#R SF  !4!4!4!Lciin!Lb!L!!L MD$hfEH{{6==)H""6H#45==a@I  199!r-zLlama4VisionAttention.__init__Es  ++33**f.H.HH $%!!'!9!9}}d* ii0NUYZ ii0NUYZ ii0NUYZ ii >UYZ r?r@rrr rrAc R|jdd}g|d|j}|j|j|}|j |j|} |j |j|} t || |\}} |jdd}| jdd} | jdd} t} |jjdvr^|jjdk(r(|jddrtjd nt|jj} | ||| | df|jsd n |j ddd |\} } | j"g|dj%} |j'| } | | fS) NrC)rr!r+)rrrrFrr)rrr)rrrrFrrr'rrr(rrrrrrrrrr)r<r@rrr rrrr rrr#rrs r>rMzLlama4VisionAttention.forwardTs$))#2. 88b8$--8 {{=166|D [[/44\B {{=166|D #:<^f#g j#--a3 ))!Q/ #--a3 (F ;; + +3N N{{//69fjjI\^c>d##L '>dkk>^>^&_#$7      % $}}C$2H2H %  % ! \*k));;;;FFH kk+. L((r?r$)rNrOrPr r-r5rQrr rrrrMrRrSs@r>r)r)Ds [1 [&26*. .)||.),,.)!. .) ! .) -. .) u||Xell3XeELL>Q5RR S.)r?r)cVeZdZfdZdej dej fdZxZS)Llama4VisionMLPc&t|||_tj|_tj |j|jd|_ tj |j|jd|_ y)NTrX) r,r-r(r3rr]rZr1r0rrr;s r>r-zLlama4VisionMLP.__init__se  WWY99V//1I1IPTU99V55v7I7IPTUr?r@rAcl|j|}|j|}|j|}|Sr_)rr]rrs r>rMzLlama4VisionMLP.forwards4/ **=9 / r?rrSs@r>r/r/s$VU\\ellr?r/c eZdZdeffd Z ddej dej deej deefdZ xZ S) Llama4VisionEncoderLayerr(ct||j|_t||_t ||_tj|j|_ tj|j|_ yr_) r,r-r1r)r*r/rr3rJr2r3r;s r>r-z!Llama4VisionEncoderLayer.__init__sb !--.v6"6*!||F,>,>?(* V5G5G(H%r? hidden_staterrrc|}|j|}|j|||\}}||z}|}|j|}|j|}||z}|f}|r||fz }|S)N)rr)r2r*r3r)r<r5rrrr9rr<s r>rMz Llama4VisionEncoderLayer.forwards ++L9 %)^^ )&4& " l  ,.  44\B xx - ,. /    &Gr?r$) rNrOrPr r-r5rQrr,rMrRrSs@r>r3r3sZI1I26,0 ll,,!.  $D> r?r3ceZdZdZdeffd Z d dejdejdeejdee dee d ee d e e e ffd Z xZS) Llama4VisionEncoderz Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Llama4VisionEncoderLayer`]. Args: config: Llama4VisionConfig r(ct|||_tjt |j Dcgc] }t|c}|_d|_ ||_ycc}w)NF) r,r-r(r3rbrcrdr3rerh)r<r(_r=s r>r-zLlama4VisionEncoder.__init__sW  mmuU[UmUmOn$o!%=f%E$op &+# %psA*r@rrrrrrsrAc||n|jj}||n|jj}||n|jj}|rdnd}|rdnd}|jD]^} |r||fz}|j r,|j r |j| j||||} n | ||||} |r || dfz}| d}`|r||fz}|std|||fDSt|||S)ad Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Nr8)r5rrrr!rc3&K|] }|| ywr_r8.0vs r> z.Llama4VisionEncoder.forward..seqWXWderwr@rx) r(rrrryrerhrrrrr) r<r@rrrrrrsencoder_statesall_attentions encoder_layerrs r>rMzLlama4VisionEncoder.forwards6>2C1N-TXT_T_TqTq$8$D $++JjJj &1%>Ne]NN$See e+>Vd  r?NNNN)rNrOrPr%r r-r5rQrr,rrrrMrRrSs@r>r8r8s126,0/3&*G ||G ,,G !. G $D> G 'tn G d^G  uo% &G r?r8cVeZdZfdZdej dej fdZxZS)Llama4UnfoldConvolutionc<t||j}t|tr||f}t j j||j|_t j|j|dz|dz|jd|_ y)N) kernel_sizestriderr!FrX) r,r-r rrr5r3UnfoldunfoldrZ num_channelsr1linear)r<r(rJr=s r>r-z Llama4UnfoldConvolution.__init__s '' k3 '& 4Khhoo+fFWFWoX ii   +a. 0;q> A     r?r@rAcp|j|}|jddd}|j|}|S)Nrr+r!)rMrrOrs r>rMzLlama4UnfoldConvolution.forward's8 M2 %--aA6  M2 r?rrSs@r>rHrHs#  U\\ellr?rHc$eZdZfdZdZxZS)Llama4VisionRotaryEmbeddingct ||j|jz}t j |dztj j|dzd}t j||ddgd}d|d<||z}||z}|j|jzdz}d|jt j d|dd|dzj|z zz }|dzd |ddddfzjdd }|dzd |ddddfzjdd } t j|| gd jjd dddf} | j|jd dddkd} t j t j"t j$| t j&| gd } | |_y) Nr+)rr!rrDr)rCrCr).NrC.)r,r- image_sizer r5rint32rcatr1r rope_thetarqrepeat_interleaverrrstackcossinr) r<r(idximg_idx frequencies_x frequencies_yfreq_dim rope_freqfreqs_xfreqs_yrrr=s r>r-z$Llama4VisionRotaryEmbedding.__init__/s 6#4#44,,sAvU[[9AA#q&!L))Wgbqk2:# 3 %%)C)CCqH6,,a11MN_QY]^Q^1`1f1f1hks1stu !A%y1IdD!m4LL__`agi_j!A%y1IdD!m4LL__`agi_j 7G,"5;;=HHJ3PSRSPS8T!!'//"a";a"?C((eii6F RWHX5Y_a)bc  r?cL|jj|jSr_)rrrrs r>rMz#Llama4VisionRotaryEmbedding.forward@s}} 4 455r?rarSs@r>rRrR.s !"6r?rRceZdZdZdgZeZdeffd ZdZ dde jde e jde e d e e d e e d e eee jd fff d ZxZS)rL vision_modelr3r(crt|||j|_|j|_|j|_|j |_|j|jzdzdz|_|jdz|_t||_ tj|jtj|jz|_tj|jtj|j |jz|_t!||_tj$|j|_tj$|j|_t+||_t/||_|j3y)Nr+r!r)r,r-rTr r1rNr rNrHpatch_embeddingr3r4r5randnrMrOrRrotary_embeddingrJ layernorm_prelayernorm_postr8r]rvision_adapterrir;s r>r-zLlama4VisionModel.__init__IsA   ++ ++!--"// OOt>1DqH''- 6v>!||DJJTEUEU9V,VW(* TZZ%++dN^N^`d`p`pBq5q(r% ;F C \\$*:*:; ll4+;+;<)0 9&A r?c|jS)zg This function is used to fetch the first embedding layer to activate grads on inputs. )rhrus r>rlz&Llama4VisionModel.get_input_embeddingsbs###r? pixel_valuesrrrrrsrA.c ||n|jj}||n|jj}||n|jj}|j\}}}} d} d} |j |} | j\} }}| j || z| z||} |jj| jdd| jd}tj| |gd} |dz }| j || z| ||} |jj| j| j}| |z} |j| } | j!|d|} |j#|}|j%| d|||}|j&} |j)| } | ddddddf} |j+| } |r |j,nd}|r|d}nd}|st/d | ||fDSt1| || S) a Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, MllamaVisionModel >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision" >>> model = MllamaVisionModel.from_pretrained(checkpoint) >>> processor = AutoProcessor.from_pretrained(checkpoint) >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> output = model(**inputs) >>> print(output.last_hidden_state.shape) torch.Size([1, 1, 4, 1025, 7680]) ``` Nr!rrCrD)rr)rrrrrr+c3&K|] }|| ywr_r8r=s r>r@z,Llama4VisionModel.forward..s_qQRQ^_rArB)r(rrrryrrhrrMrr5rVrOrrrrkrFrjr]rwrlrmr@rr)r<rorrrrrsbatch_size_times_num_tilesrNr rnum_concurrent_media num_chunksr5r:r rrMpositional_embeddingrr~r@rxs r>rMzLlama4VisionModel.forwardhsS>2C1N-TXT_T_TqTq$8$D $++JjJj &1%rLrLDs&34%L12$26,0/3&* _ ll_ !._ $D> _ 'tn _ d^ _  ellC&7 88 9_ r?rLc(eZdZddgZiZdZeZdeffd ZdZ dZ dZ d Z d Z d Zd ej d eeeefdefdZe d&dej.d ej deej2deej.deeej deej d eeeeefdeedeej.deedeedeedeedeej.deeej2fdej2deedeeeff$dZ d'd Z e!dej2d!ed"ed#ejDdej2d$ef d%Z#xZ$S)(Llama4ForConditionalGenerationr(r3r(cht||t|j|_t ||_t|j|_ |jj|_ |jj|jjnd|_ |jy)NrC)r,r-rLrrfrmulti_modal_projectorrrDrr`r(r_rir;s r>r-z'Llama4ForConditionalGeneration.__init__s  -f.B.BC%>v%F"/0B0BC ,,778< 8P8P8\DKK44bd r?c6|jjSr_)rrlrus r>rlz3Llama4ForConditionalGeneration.get_input_embeddingss""7799r?c:|jj|yr_)rrorns r>roz3Llama4ForConditionalGeneration.set_input_embeddingss 007r?c6|jjSr_)rrrus r>rz4Llama4ForConditionalGeneration.get_output_embeddingss""88::r?c:|jj|yr_)rrrs r>rz4Llama4ForConditionalGeneration.set_output_embeddingss 11.Ar?c:|jj|yr_)rrrs r>rz*Llama4ForConditionalGeneration.set_decoders ''0r?c6|jjSr_)rrrus r>rz*Llama4ForConditionalGeneration.get_decoders""..00r?rovision_feature_layervision_feature_select_strategyc |dvrtd|j|jDcic] \}}| || }}}|j|fddi|}|j}|Scc}}w)a Obtains image last hidden states from the vision tower and apply al projection. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`) The tensors corresponding to the input images. vision_feature_layer (`Union[int, List[int]]`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` Returns: image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`). )rrz$Unexpected select feature strategy: rrF)rzritemsrfrw) r<rorrrkr? image_outputsr5s r>get_image_featuresz1Llama4ForConditionalGeneration.get_image_featuress. *1D DCDDgDgChij j#)<<>C41aQ]!Q$CC))),]U]V\] $66 Ds A&A&rprrr@rqrr6rrrrsrr image_sizesrrAc | | n|jj} | | n|jj} | | n|jj} ||n|jjj }||n|jjj }|du|duz r td| | td||j|}|b|j||||}|j}|jd|jd}|j|}||jjk(jd}|j!|j"}|jd|jd}|dj%d}|j'}||jdk7r td|d |jd|jdj)d|jd}|j+||}|j|}|j,d||||| | | | ||d |}|d}d}| <||dd|jd d z dfj!|j"}|d ddddf|j!|j"dk7j/}| d d df|j!| j"dk7j/} n1|d ddddfj/}| d d dfj/} t1j2}!|!|jd|jd| jdj!|j"}| s|f|d dz}"||f|"zS|"St5|||j6|j8|j:| Sd S)a labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, LlavaForConditionalGeneration >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> prompt = "USER: \nWhat's the content of the image? ASSISTANT:" >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, text=prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_new_tokens=15) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed" ```NrvzdYou cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one)rorrrrC).rrzMismatch: final_mask wants z0 embeddings, but multi_modal_projector returned ) rrr@rqr6rrrrsrrr!.)rrr@r@rxrr8)r(rrrryrrrrzrlrrrFrrzimage_token_idr}rrrsumrmasked_scatterrrr3CrossEntropyLossrr@r@rx)#r<rprorrr@rqrrrr6rrrrsrrrrroriginal_inputs_embeds_shape vision_flatprojected_vision_flatspecial_image_mask final_mask final_mask_1dnum_tokens_to_fill expanded_maskr<rrshift_attention_mask shift_logits shift_labelsloss_fctr~s# r>rMz&Llama4ForConditionalGeneration.forward sab2C1N-TXT_T_TqTq$8$D $++JjJj &1%+> ((--b.2E2Eb2IJK$($>$>{$K !"+t{{/I/I"I!T!TUW!X +..}/C/CDJ)..r=3E3Eb3IJM&v.66r:M!.!2!2!4 !%:%?%?%BB 12D1EF::O:T:TUV:W9XZ *33B7>>r=CUCUVXCYZM)88H]^M)../KLM%$%%  )%+'/!5#))      )(6a6<<?Q;N9O9Q6Q'R'U'UV\VcVc'd$%c3B3k23G3J3J6==3Y]^3^_jjl %c12g/C/F/Fv}}/UYZ/Z[ffh %c3B3k2==? %c12g99; **,H!!"l&7&7&;O>OPR>S>V>VWcWjWj>kDY,F'+'7D7V# CV C+#33!//))2>2J   QU   r?c f|jj|f|||||d|} |ddk(r|| d<| S)N)r@rqrrrrro)rprepare_inputs_for_generation) r<rpr@rqrorrrr model_inputss r>rzrzTLlama4ForConditionalGeneration._prepare_4d_causal_attention_mask_with_cache_positions<  %.*<*<*>!*C(K*' E*..I** -0Ye\j\q\qK!##jjqA 5<< n>S>STWeWmWmnprsWtt tK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDg&&E   ,q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r?)NNNNNNNNNNNNNNrN)NNNNNN)%rNrOrPrrrr"rQr-rlrorrrrr5r=rrrrrrr&rrQr,rrrrrMrrrrrRrSs@r>rwrws13MNHL | :8;B11''$CcN3), <'+*.1537=A59@D8<-1$(,0/3&*5934$(#U ##U ''U !. U u//0 U "$u'8'8"9: U   1 12U 'uS$s)^'<=U )1 U ))*U D>U $D>U 'tnU d^U !!1!12U c5<</0!U "\\#U $*+%U & u22 3'U U t<4 444{{ 4  4  44r?rw)r?r\rLrrw)r)`r dataclassesrtypingrrrrrr5torch.nnr3torch.nn.functionalrr/transformers.models.llama4.configuration_llama4r activationsr cache_utilsr r r generationrintegrations.hub_kernelsrmodeling_attn_mask_utilsrmodeling_flash_attention_utilsrmodeling_outputsrrrrmodeling_rope_utilsrrmodeling_utilsrrprocessing_utilsrutilsrrrrr configuration_llama4r"r#!torch.nn.attention.flex_attentionr$integrations.flex_attentionr% get_loggerrNrModuler'rUrcrxrrrQrrrrqrrrr(r?r\rrrrrrrr!r'r)r/r3r8rHrRrLrw__all__r8r?r>rs  !99 N!BB)C>BmmKF&hh@ !;J   H % B)BII)$ !uxx != =(_-)"BII)".)"X > 2  2  2|| 2 5<< %& 2 UU\\ U# U%,, U(% II% <<% % << % U\\* %  %%D% II% <<% % << % U\\* %  %%4`)"))`)FFRYYFR$KO$K$KN\+\\~ ?,j>m -m ` $<;$< $)BII>)B bii )ryy)XW "))W tbii(6"))6,C -C Ll%:Ol^  r?