
    <hP                     *   S SK r S SKJrJrJr  S SKrS SKJr  SSKJ	r	  SSK
JrJr  SSKJr  SSKJr  SSKJrJrJr  SS	KJrJr  SS
KJrJr  SSKJrJr  SSKJr  SSK J!r!J"r"J#r#  SSK$J%r%  SSK&J'r'   " S S\RP                  5      r) " S S\RP                  5      r* " S S\RP                  5      r+S\RX                  S\-S\RX                  4S jr. S5S\RP                  S\RX                  S\RX                  S\RX                  S\\RX                     S \/S!\/S"\\!   4S# jjr0S$ r1S6S% jr2 " S& S'\RP                  5      r3 " S( S)\5      r4\" " S* S+\5      5       r5\" " S, S-\55      5       r6\" " S. S/\5\5      5       r7 " S0 S1\\55      r8 " S2 S3\\55      r9/ S4Qr:g)7    N)CallableOptionalUnion   )ACT2FN)CacheDynamicCache)GenerationMixin)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)check_model_inputs   )HeliumConfigc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )HeliumRMSNorm.   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g N)super__init__nn	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      b/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/helium/modeling_helium.pyr"   HeliumRMSNorm.__init__/   s-    ll5::k#:; #    c                 V   UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  R                  [        R                  5      U-  R                  U5      $ )N   T)keepdim)	dtypetor%   float32powmeanrsqrtr(   r'   )r)   hidden_statesinput_dtypevariances       r-   forwardHeliumRMSNorm.forward4   s    #))%((7 $$Q',,R,>%H?T?T4T(UUu}}-=AA+NNr/   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler'   shaper(   r)   s    r-   
extra_reprHeliumRMSNorm.extra_repr;   s*    ))*+6$2G2G1HIIr/   )r(   r'   )gư>)	__name__
__module____qualname____firstlineno__r"   r=   rC   __static_attributes____classcell__r,   s   @r-   r   r   .   s    $
OJ Jr/   r   c                   l   ^  \ rS rSrSS\4U 4S jjjr\R                  " 5       \S 5       5       r	Sr
U =r$ )HeliumRotaryEmbedding?   configc                   > [         TU ]  5         [        US5      (       aZ  [        UR                  [
        5      (       a;  UR                  R                  SUR                  R                  S5      5      U l        OSU l        UR                  U l	        UR                  U l
        Xl        [        U R                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                   U l        g )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r!   r"   hasattr
isinstancerQ   dictgetrR   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrO   r   rope_init_fnattention_scalingregister_bufferrU   original_inv_freq)r)   rO   devicerU   r,   s       r-   r"   HeliumRotaryEmbedding.__init__@   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r/   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r2   r   mpscpuF)device_typeenabledr1   dim)r4   )rU   floatexpandrA   r5   rb   rX   rS   strr%   autocast	transposecatcosr_   sinr4   )
r)   xposition_idsinv_freq_expandedposition_ids_expandedrg   freqsembrq   rr   s
             r-   r=   HeliumRotaryEmbedding.forwardQ   sR    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E'E!((--[`J`ahhmmfk^^UC&,,.1F1L1L1NNYYZ[]^_E))UN3C'')d444C'')d444C	 D vvAGGv$cff177f&;;; DCs   $BF  
F.)r_   rO   r\   ra   r]   r^   rR   r    )rE   rF   rG   rH   r   r"   r%   no_gradr   r=   rI   rJ   rK   s   @r-   rM   rM   ?   s6    /| / /" ]]_<  <r/   rM   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )	HeliumMLPa   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  UR                  S9U l        [
        R                  " U R                  U R                  UR                  S9U l	        [
        R                  " U R                  U R                  UR                  S9U l
        [        UR                     U l        g )Nbias)r!   r"   rO   r*   intermediate_sizer#   Linearmlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr)   rO   r,   s     r-   r"   HeliumMLP.__init__b   s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r/   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r    )r   r   r   r   )r)   rs   r   s      r-   r=   HeliumMLP.forwardl   s6    NN4;;t~~a/@#ADLLQRO#ST	r/   )r   rO   r   r   r*   r   r   )rE   rF   rG   rH   r"   r=   rI   rJ   rK   s   @r-   r|   r|   a   s    0 r/   r|   r:   n_repreturnc                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r   N)rA   rl   reshape)r:   r   batchnum_key_value_headsslenhead_dims         r-   	repeat_kvr   q   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr/   modulequerykeyvalueattention_maskscalingdropoutkwargsc                 @   [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub"  US S 2S S 2S S 2S UR
                  S   24   nX-   n
[        R                  R                  U
S[        R                  S9R                  UR                  5      n
[        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr1   r   r2   )rj   r4   )ptrainingr   )r   num_key_value_groupsr%   matmulro   rA   r#   
functionalsoftmaxr6   r5   r4   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r-   eager_attention_forwardr   }   s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r/   c                 x    U SSSS24   nU SSSS24   n[         R                  " U* U4SS9R                  S5      $ )	z*Rotates half the hidden dims of the input..r   Nr1   r   r2   ri   r   )r%   stackflatten)rs   x1x2s      r-   rotate_halfr      sJ    	
319B	
319B;;Ryb)11"55r/   c                 4   UR                  U5      nUR                  U5      nUSSUR                  S   S-  24   R                  SSS9nUSSUR                  S   S-  24   R                  SSS9nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )a  Applies Rotary Position Embedding to the query and key tensors.

Args:
    q (`torch.Tensor`): The query tensor.
    k (`torch.Tensor`): The key tensor.
    cos (`torch.Tensor`): The cosine part of the rotary embedding.
    sin (`torch.Tensor`): The sine part of the rotary embedding.
    position_ids (`torch.Tensor`, *optional*):
        Deprecated and unused.
    unsqueeze_dim (`int`, *optional*, defaults to 1):
        The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
        sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
        that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
        k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
        cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
        the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
    `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
.Nr2   r1   ri   )	unsqueezerA   repeat_interleaver   )qkrq   rr   rt   unsqueeze_dimq_embedk_embeds           r-   apply_rotary_pos_embr      s    ( --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
ECw;q>C/0Gw;q>C/0Gr/   c                   (  ^  \ rS rSrSrSS\S\\   4U 4S jjjr  SS\	R                  S\\	R                  \	R                  4   S\\	R                     S	\\   S
\\	R                     S\\   S\\	R                  \	R                  4   4S jjrSrU =r$ )HeliumAttention   z=Multi-headed attention from 'Attention Is All You Need' paperrO   	layer_idxc                 J  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        S[        R                  " U R                  5      -  U l        UR                  U l        SU l        [        R                   " UR
                  UR                  U R                  -  UR"                  S9U l        [        R                   " UR
                  UR                  U R                  -  UR"                  S9U l        [        R                   " UR
                  UR                  U R                  -  UR"                  S9U l        [        R                   " UR
                  UR
                  SS9U l        g )Nr   r   Tr   F)r!   r"   rO   r   getattrr*   num_attention_headsr   r   r   mathsqrtr   attention_dropout	is_causalr#   r   attention_biasq_projk_projv_projo_projr)   rO   r   r,   s      r-   r"   HeliumAttention.__init__   s?   "
F4F4F&JdJd4de$*$>$>&B\B\$\!499T]]33!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii 2 2F4F4FUSr/   r:   position_embeddingsr   past_key_valuecache_positionr   r   c                 4   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nUu  p[        XX5      u  pUb$  XUS.nUR                  XU R                  U5      u  p[        nU R                  R                  S:w  a  [        U R                  R                     nU" U U	U
UU4U R                  (       d  SOU R                  U R                   S.UD6u  nnUR"                  " / UQSP76 R%                  5       nU R'                  U5      nUU4$ )Nr2   r   r1   )rr   rq   r   eager        )r   r   )rA   r   r   viewro   r   r   r   updater   r   rO   _attn_implementationr   r   r   r   r   r   r   )r)   r:   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rq   rr   cache_kwargsattention_interfacer   r   s                     r-   r=   HeliumAttention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST&#7RU#[ %#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHkk+.L((r/   )r   rO   r   r   r   r   r   r   r   r   r   r    )NN)rE   rF   rG   rH   __doc__r   r   intr"   r%   Tensorr@   r   
LongTensorr   r   r=   rI   rJ   rK   s   @r-   r   r      s    GT| T T T4 +/59))||)) #5<<#=>)) !.	))
 !)) !!1!12)) +,)) 
u||U\\)	*)) ))r/   r   c                   B  ^  \ rS rSrSS\S\\   4U 4S jjjr      SS\R                  S\\R                     S\\R                     S\\   S	\\   S
\\R                     S\\\R                  \R                  4      S\\   S\\R                     4S jjrSrU =r$ )HeliumDecoderLayeri  rO   r   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)rO   r   r+   )r!   r"   r*   r   	self_attnr|   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r-   r"   HeliumDecoderLayer.__init__  sj    !--(LV$,V-?-?VEXEXY(5f6H6HfNaNa(b%r/   r:   r   rt   r   	use_cacher   r   r   r   c                     Un	U R                  U5      nU R                  " SUUUUUUUS.UD6u  pX-   nUn	U R                  U5      nU R                  U5      nX-   nU$ )N)r:   r   rt   r   r   r   r    )r   r   r   r   )r)   r:   r   rt   r   r   r   r   r   residual_s              r-   r=   HeliumDecoderLayer.forward  s     !,,];>> 	
')%)) 3	
 	
 !0 !55mD/ 0r/   )r*   r   r   r   r   r    )NNNFNN)rE   rF   rG   rH   r   r   r   r"   r%   r   r   r   boolr@   r   r   r=   rI   rJ   rK   s   @r-   r   r     s    c| c c c 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	 r/   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
HeliumPreTrainedModeli0  rO   modelTr   past_key_values)r:   
attentionsr   N)rE   rF   rG   rH   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrI   r   r/   r-   r   r   0  sQ    &*#-.#4"5N!"&+%r/   r   c                     ^  \ rS rSrS\4U 4S jjr\\       SS\\	R                     S\\	R                     S\\	R                     S\\   S\\	R                     S	\\	R                     S
\\   S\\   S\4S jj5       5       rSrU =r$ )HeliumModeliC  rO   c           	      
  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ sH  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        [#        U5      U l        SU l        U R)                  5         g s  snf )Nr   F)r!   r"   pad_token_idpadding_idx
vocab_sizer#   	Embeddingr*   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normrM   
rotary_embgradient_checkpointing	post_initr   s      r-   r"   HeliumModel.__init__E  s     !.. ++LL):):F<N<NPTP`P`ammDI&JbJbDcdDcy2Dcd
 "&"4"4&:M:MN	/7&+# 	 es   D 	input_idsr   rt   r   inputs_embedsr   r   r   r   c           
      8   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc
  [        5       nUcD  Ub  UR                  5       OSn	[        R
                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U R                  UUUUUS9n
UnU R                  X5      nU R                  S U R                  R                    H  nU" U4U
UUUUS.UD6nM     U R                  U5      n[        UUS9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )rb   )rO   input_embedsr   r   r   rt   )r   rt   r   r   r   )last_hidden_stater   )
ValueErrorr	  r	   get_seq_lengthr%   arangerA   rb   r   r   rO   r  r  r  r  r   )r)   r  r   rt   r   r  r   r   r   past_seen_tokensr   r:   r   decoder_layers                 r-   r=   HeliumModel.forwardU  sK    -t";<YZZ *.*;*;I*FM0*nO!CRC^==?de+0<< ]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oomJ![[)H4;;+H+HIM)*).-$7 M J 		-0&++
 	
r/   )r	  r  r  r  r  r  r  )NNNNNNN)rE   rF   rG   rH   r   r"   r   r   r   r%   r   r   r   FloatTensorr   r   r   r   r=   rI   rJ   rK   s   @r-   r  r  C  s    |    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r/   r  c                   ~  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jrS rS	 r	\
\         SS
\\R                     S\\R                     S\\R                     S\\   S\\R"                     S\\R                     S\\   S\\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )HeliumForCausalLMi  zlm_head.weightlm_headcolwise_repr:   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g )NFr   )
r!   r"   r  r   r  r#   r   r*   r!  r  r   s     r-   r"   HeliumForCausalLM.__init__  sU      (
 ++yy!3!3V5F5FUS 	r/   c                     Xl         g r    r   )r)   decoders     r-   set_decoderHeliumForCausalLM.set_decoder  s    
r/   c                     U R                   $ r    r'  rB   s    r-   get_decoderHeliumForCausalLM.get_decoder  s    zzr/   r  r   rt   r   r  labelsr   r   logits_to_keepr   r   c
                 ~   U R                   " SUUUUUUUS.U
D6nUR                  n[        U	[        5      (       a  [	        U	* S5      OU	nU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U
D6n[        UUUR                  UR                  UR                  S9$ )a   
Example:

```python
>>> from transformers import AutoTokenizer, HeliumForCausalLM

>>> model = HeliumForCausalLM.from_pretrained("google/helium-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/helium-7b")

>>> prompt = "What is your favorite condiment?"
>>> 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]
"What is your favorite condiment?"
```)r  r   rt   r   r  r   r   N)r#  r.  r  )lossr#  r   r:   r   r   )r   r  rX   r   slicer!  loss_functionrO   r  r   r   r:   r   )r)   r  r   rt   r   r  r.  r   r   r/  r   outputsr:   slice_indicesr#  r1  s                   r-   r=   HeliumForCausalLM.forward  s    @ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r/   )r!  r   r  )	NNNNNNNNr   )rE   rF   rG   rH   _tied_weights_keys_tp_plan_pp_planr"   r)  r,  r   r   r   r%   r   r   r   r  r   r   r   r   r   r   r=   rI   rJ   rK   s   @r-   r   r     s:   *+=)H_-z:;H  151537+/59-1$(59348
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 ))*8
 D>8
 !!1!128
 c5<</08
 +,8
 
 8
  8
r/   r   c                       \ rS rSrSrg)HeliumForSequenceClassificationi  r   NrE   rF   rG   rH   rI   r   r/   r-   r;  r;        r/   r;  c                       \ rS rSrSrg)HeliumForTokenClassificationi  r   Nr<  r   r/   r-   r?  r?    r=  r/   r?  )r   r  r   r;  r?  )r   )Nr   );r   typingr   r   r   r%   torch.nnr#   activationsr   cache_utilsr   r	   
generationr
   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   configuration_heliumr   Moduler   rM   r|   r   r   r   rk   r   r   r   r   r   r   r  r   r;  r?  __all__r   r/   r-   <module>rP     s  ,  , ,   ! . ) / 
 P K F & I I / .JBII J"<BII <D		  	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%46BA)bii A)H*3 *Z O  $ K
' K
 K
\ N
- N
 N
b	&FH] 		#@BW 	r/   