
    <hYY                     p   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  SS
KJr  SSKJr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$J%r%J&r&J'r'  SSK(J)r)  SSK*J+r+  \" S5       " S S\RX                  5      5       r- " S S\RX                  5      r.S r/S;S jr0S\Rb                  S\2S\Rb                  4S jr3 S<S\RX                  S \Rb                  S!\Rb                  S"\Rb                  S#\\Rb                     S$\4S%\4S&\#\%   4S' jjr5 " S( S)\RX                  5      r6 " S* S+\5      r7\& " S, S-\!5      5       r8 " S. S/\RX                  5      r9\& " S0 S1\85      5       r:\& " S2 S3\8\5      5       r; " S4 S5\\85      r< " S6 S7\\85      r= " S8 S9\\85      r>/ S:Qr?g)=    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GenericForQuestionAnswering 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   )Qwen3ConfigRMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )Qwen3RMSNorm0   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z+
Qwen3RMSNorm is equivalent to T5LayerNorm
N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      `/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/qwen3/modeling_qwen3.pyr'   Qwen3RMSNorm.__init__2   s/     	ll5::k#:; #    c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )N   T)keepdim)	dtypetor)   float32powmeanrsqrtr,   r+   )r-   hidden_statesinput_dtypevariances       r1   forwardQwen3RMSNorm.forward:   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r3   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tupler+   shaper,   r-   s    r1   
extra_reprQwen3RMSNorm.extra_reprA   s*    ))*+6$2G2G1HIIr3   )r,   r+   )gư>)	__name__
__module____qualname____firstlineno__r'   rA   rG   __static_attributes____classcell__r0   s   @r1   r#   r#   0   s    $;J Jr3   r#   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )Qwen3MLPE   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFbias)r&   r'   configr.   intermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnr-   rW   r0   s     r1   r'   Qwen3MLP.__init__F   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r3   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ N)r\   r^   rZ   r[   )r-   xr\   s      r1   rA   Qwen3MLP.forwardP   s6    NN4;;t~~a/@#ADLLQRO#ST	r3   )r^   rW   r\   rZ   r.   rX   r[   )rI   rJ   rK   rL   r'   rA   rM   rN   rO   s   @r1   rQ   rQ   E   s    0 r3   rQ   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..Nr6   r5   dim)rE   r)   cat)rc   x1x2s      r1   rotate_halfrk   U   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r3   c                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )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.
)	unsqueezerk   )qkcossinposition_idsunsqueeze_dimq_embedk_embeds           r1   apply_rotary_pos_embrv   \   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr3   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)rE   expandreshape)r>   rw   batchnum_key_value_headsslenhead_dims         r1   	repeat_kvr   w   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr3   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$ )Nr5   r   r6   )rg   r8   )ptrainingr   )r   num_key_value_groupsr)   matmul	transposerE   r   
functionalsoftmaxr:   r9   r8   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r1   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r3   c                   F  ^  \ rS rSrSrS\S\4U 4S jjr  SS\R                  S\
\R                  \R                  4   S\\R                     S	\\   S
\\R                     S\\   S\
\R                  \\R                     \\
\R                        4   4S jjrSrU =r$ )Qwen3Attention   z=Multi-headed attention from 'Attention Is All You Need' paperrW   	layer_idxc                 4  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  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                  -  UR
                  UR                  S9U l        [)        U R                  UR*                  S9U l        [)        U R                  UR*                  S9U l        UR0                  U   S:X  a  UR2                  U l        g S U l        g )Nr   g      TrU   r/   sliding_attention)r&   r'   rW   r   getattrr.   num_attention_headsr   r}   r   r   attention_dropout	is_causalr   rY   attention_biasq_projk_projv_projo_projr#   rms_norm_epsq_normk_normlayer_typessliding_windowr-   rW   r   r0   s      r1   r'   Qwen3Attention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 #4==f6I6IJ"4==f6I6IJ7=7I7I)7TXk7kf33qur3   r>   position_embeddingsr   past_key_valuecache_positionr   rx   c                    UR                   S S n/ UQSPU R                  P7nU R                  U R                  U5      R	                  U5      5      R                  SS5      n	U R                  U R                  U5      R	                  U5      5      R                  SS5      n
U R                  U5      R	                  U5      R                  SS5      nUu  p[        XX5      u  pUb$  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$                  U R&                  S.UD6u  nnUR(                  " / UQSP76 R+                  5       nU R-                  U5      nUU4$ )Nr6   r   r5   )rq   rp   r   eager        )r   r   r   )rE   r   r   r   viewr   r   r   r   rv   updater   r   rW   _attn_implementationr   r   r   r   r   r{   r   r   )r-   r>   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rp   rq   cache_kwargsattention_interfacer   r   s                     r1   rA   Qwen3Attention.forward   s    $))#2.88b8$--8{{4;;}#=#B#B<#PQ[[\]_`a[[]!;!@!@!NOYYZ[]^_
{{=166|DNNqRST&#7RU#[ %#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL..
%
 
%
!\ "));;;;FFHkk+.L((r3   )r   rW   r   r   r   r   r   r   r   r   r   r   r   r   )NN)rI   rJ   rK   rL   __doc__r    intr'   r)   TensorrD   r   r	   
LongTensorr   r   rA   rM   rN   rO   s   @r1   r   r      s    Gv{ vs v> +/59*)||*) #5<<#=>*) !.	*)
 !*) !!1!12*) -.*) 
u||Xell3XeELL>Q5RR	S*) *)r3   r   c                   8  ^  \ rS rSrS\S\4U 4S jjr      SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\R                     S\	\\R                  \R                  4      S\\   S\\R                     4S jjrSrU =r$ )Qwen3DecoderLayer   rW   r   c                 4  > [         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
        UR                  U   U l        g )N)rW   r   r   )r&   r'   r.   r   	self_attnrQ   mlpr#   r   input_layernormpost_attention_layernormr   attention_typer   s      r1   r'   Qwen3DecoderLayer.__init__   s}    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%$00;r3   r>   r   rr   r   	use_cacher   r   r   rx   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$ )N)r>   r   rr   r   r   r   r    )r   r   r   r   )r-   r>   r   rr   r   r   r   r   r   residual_s              r1   rA   Qwen3DecoderLayer.forward   s     !,,];>> 	
')%)) 3	
 	
 !0 !55mD/ 0r3   )r   r.   r   r   r   r   )NNNFNN)rI   rJ   rK   rL   r    r   r'   r)   r   r   r   r	   boolrD   r   r   rA   rM   rN   rO   s   @r1   r   r      s    	<{ 	<s 	< 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	 r3   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	)
Qwen3PreTrainedModeli  rW   modelTr   past_key_values)r>   
attentionsr   N)rI   rJ   rK   rL   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_outputsrM   r   r3   r1   r   r     sQ    &*#,-#4"5N!"&*$r3   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$ )Qwen3RotaryEmbeddingi(  rW   c                   > [         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
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrW   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)r-   rW   devicer   r0   s       r1   r'   Qwen3RotaryEmbedding.__init__)  s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r3   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r6   r   mpscpuF)device_typeenabledr5   rf   )r8   )r   floatrz   rE   r9   r   r   r   strr)   autocastr   rh   rp   r   rq   r8   )
r-   rc   rr   inv_freq_expandedposition_ids_expandedr   freqsembrp   rq   s
             r1   rA   Qwen3RotaryEmbedding.forward:  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   $BF  
F.)r   rW   r   r   r   r   r   rb   )rI   rJ   rK   rL   r    r'   r)   no_gradr   rA   rM   rN   rO   s   @r1   r   r   (  s6    /{ / /" ]]_<  <r3   r   c                     ^  \ rS rSrS\4U 4S jjr\\       SS\\	R                     S\\	R                     S\\	R                     S\\   S\\	R                     S	\\   S
\\	R                     S\\   S\4S jj5       5       rSrU =r$ )
Qwen3ModeliJ  rW   c           	      B  > [         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S9U l        SU l        SU R(                  R*                  ;   U l        U R/                  5         g s  snf )Nr   )rW   Fr   )r&   r'   pad_token_idpadding_idx
vocab_sizer   	Embeddingr.   embed_tokens
ModuleListrangenum_hidden_layersr   layersr#   r   normr   
rotary_embgradient_checkpointingrW   r   has_sliding_layers	post_initr   s      r1   r'   Qwen3Model.__init__L  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcCbiv1Cbc
 !!3!39L9LM	.f=&+#"59P9P"P 	 ds   D	input_idsr   rr   r   inputs_embedsr   r   r   rx   c                    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=n
[        5      (       d?  U R                  UUUUUS.nS[        S
0 UD60n
U R                  (       a  [        S
0 UD6U
S'   UnU R                  X5      nU R                   S U R                  R"                    H  nU" U4XR$                     UUUUUS.UD6nM!     U R'                  U5      n[)        UU(       a  US	9$ S S	9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )r   )rW   input_embedsr   r   r   rr   full_attentionr   )r   rr   r   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r
   get_seq_lengthr)   arangerE   r   rm   r   r   rW   r   r  r   r  r  r  r   r  r   )r-   r  r   rr   r   r  r   r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr>   r   decoder_layers                  r1   rA   Qwen3Model.forward]  s    -t";<YZZ  --i8M0*nO!CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L ?-FF ++ -"0"0#2 ,K !"4"C{"C# &&;\;k_j;k#$78% #oomJ![[)H4;;+H+HIM)	23O3OP).#-$7	 	M J 		-0&+/8O
 	
>B
 	
r3   )r  r  r  r  r  r  r  r  )NNNNNNN)rI   rJ   rK   rL   r    r'   r   r   r   r)   r   r   r	   FloatTensorr   r   r   r   rA   rM   rN   rO   s   @r1   r	  r	  J  s    { "  151537+/59$(59E
E,,-E
 !.E
 u//0	E

 "%E
   1 12E
 D>E
 !!1!12E
 +,E
 
!E
  E
r3   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$ )Qwen3ForCausalLMi  zlm_head.weightlm_headcolwise_repr>   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g rT   )
r&   r'   r	  r   r  r   rY   r.   r+  r  r_   s     r1   r'   Qwen3ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r3   c                     Xl         g rb   r   )r-   decoders     r1   set_decoderQwen3ForCausalLM.set_decoder  s    
r3   c                     U R                   $ rb   r1  rF   s    r1   get_decoderQwen3ForCausalLM.get_decoder  s    zzr3   r  r   rr   r   r  labelsr   r   logits_to_keepr   rx   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  
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, Qwen3ForCausalLM

>>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

>>> 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."
```)r  r   rr   r   r  r   r   N)r-  r8  r  )lossr-  r   r>   r   r   )r   r  r   r   slicer+  loss_functionrW   r  r   r   r>   r   )r-   r  r   rr   r   r  r8  r   r   r9  r   outputsr>   slice_indicesr-  r;  s                   r1   rA   Qwen3ForCausalLM.forward  s    J ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r3   )r+  r   r  )	NNNNNNNNr   )rI   rJ   rK   rL   _tied_weights_keys_tp_plan_pp_planr'   r3  r6  r   r   r   r)   r   r   r	   r(  r   r   r   r   r   r   rA   rM   rN   rO   s   @r1   r*  r*    s:   *+=)H_-z:;H  151537+/59-1$(5934=
E,,-=
 !.=
 u//0	=

 "%=
   1 12=
 ))*=
 D>=
 !!1!12=
 c5<</0=
 +,=
 
 =
  =
r3   r*  c                       \ rS rSrSrg)Qwen3ForSequenceClassificationi  r   NrI   rJ   rK   rL   rM   r   r3   r1   rE  rE        r3   rE  c                       \ rS rSrSrg)Qwen3ForTokenClassificationi  r   NrF  r   r3   r1   rI  rI    rG  r3   rI  c                       \ rS rSrSrSrg)Qwen3ForQuestionAnsweringi  transformerr   N)rI   rJ   rK   rL   r   rM   r   r3   r1   rK  rK    s    %r3   rK  )r*  rK  r   r	  rE  rI  )Nr   )r   )@typingr   r   r   r)   r   activationsr   cache_utilsr	   r
   
generationr   integrationsr   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   configuration_qwen3r    Moduler#   rQ   rk   rv   r   r   r   r   r   r   r   r   r   r	  r*  rE  rI  rK  __all__r   r3   r1   <module>r^     s  , - ,   ! . ) 7 R B  P K F & I I / , Y'J299 J (J(ryy  (6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4G)RYY G)T+2 +\ ?  $<299 <D Y
% Y
 Y
x S
+_ S
 S
l	%EG[ 		"?AU 	& ;=Q &r3   