
    <hU                         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"J#r#  SSK$J%r%  SSK&J'r'  \#RP                  " \)5      r*S r+S3S jr,S\RZ                  S\.S\RZ                  4S jr/ S4S\R`                  S\RZ                  S\RZ                  S\RZ                  S\\RZ                     S\1S\1S\\    4S jjr2 " S  S!\R`                  5      r3 " S" S#\R`                  5      r4 " S$ S%\5      r5 " S& S'\R`                  5      r6\! " S( S)\5      5       r7\! " S* S+\75      5       r8\! " S, S-\7\5      5       r9 " S. S/\\75      r: " S0 S1\\75      r;/ S2Qr<g)5    )CallableOptionalUnionN   )ACT2FN)CacheDynamicCache)GenerationMixin)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)check_model_inputs   )	PhiConfigc                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..N   dim)shapetorchcat)xx1x2s      \/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/phi/modeling_phi.pyrotate_halfr)   !   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''    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.
)	unsqueezer)   )qkcossinposition_idsunsqueeze_dimq_embedk_embeds           r(   apply_rotary_pos_embr5   (   sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr*   hidden_states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)r"   expandreshape)r6   r7   batchnum_key_value_headsslenhead_dims         r(   	repeat_kvr@   C   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$ )Nr   r   r   )r!   dtype)ptrainingr   )r@   num_key_value_groupsr#   matmul	transposer"   nn
functionalsoftmaxfloat32torK   rG   rM   
contiguous)rA   rB   rC   rD   rE   rF   rG   rH   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r(   eager_attention_forwardr\   O   s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$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                   <  ^  \ 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\
\R                  \\R                     \\
\R                        4   4S jjrSrU =r$ )PhiAttentioni   z=Multi-headed attention from 'Attention Is All You Need' paperconfig	layer_idxc                   > [         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                  -  SS9U l        [        R                  " UR
                  UR                  U R                  -  SS9U l        [        R                  " UR
                  UR                  U R                  -  SS9U l        [        R                  " UR                  U R                  -  UR
                  SS9U l        ['        U R                  UR(                  -  5      U l        UR,                  U l        U R,                  (       ay  [        R.                  " UR
                  UR                  -  UR0                  SS9U l        [        R.                  " UR
                  UR                  -  UR0                  SS9U l        g g )Nr?   g      Tbias)epselementwise_affine)super__init__r`   ra   getattrhidden_sizenum_attention_headsr?   r=   rN   rF   attention_dropout	is_causalrQ   Linearq_projk_projv_projdenseintpartial_rotary_factorrotary_ndimsqk_layernorm	LayerNormlayer_norm_epsq_layernormk_layernormselfr`   ra   	__class__s      r(   rh   PhiAttention.__init__l   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijYYv99DMMI6K]K]dhi
0L0L LM"//!||""f&@&@@fF[F[pt D  "||""f&@&@@fF[F[pt D	 r*   r6   position_embeddingsrE   past_key_valuecache_positionr8   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nU R                  (       a"  U R                  U	5      n	U R                  U
5      n
Uu  pU	SS U R                  24   U	SU R                  S 24   pU
SS U R                  24   U
SU R                  S 24   nn[        UUX5      u  nn[        R                  " X4SS9n	[        R                  " UU4SS9n
Ub$  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 R1                  5       nU R3                  U5      nUU4$ )
Nr   r   r   .r    )r0   r/   r   eager        )rG   rF   )r"   r?   ro   viewrP   rp   rq   rv   ry   rz   ru   r5   r#   r$   updatera   r\   r`   _attn_implementationr   rM   rl   rF   r;   rV   rr   )r|   r6   r   rE   r   r   rH   input_shapehidden_shapequery_statesrW   rX   r/   r0   	query_rot
query_passkey_rotkey_passcache_kwargsattention_interfacer[   rY   s                         r(   forwardPhiAttention.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++L9L))*5J& 1 1 1112d//112 
 s/d////0sD--//0 
 2)WcO	7 yy)!8bAYY2;
%#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHjj-L((r*   )rl   r`   rr   r?   rm   rz   rp   ra   rN   ry   ro   rv   ru   rF   rq   )NN)__name__
__module____qualname____firstlineno____doc__r   rs   rh   r#   Tensortupler   r   
LongTensorr   __static_attributes____classcell__r}   s   @r(   r^   r^   i   s    Gy S 8 +/59;)||;) #5<<#=>;) !.	;)
 !;) !!1!12;) 
u||Xell3XeELL>Q5RR	S;) ;)r*   r^   c                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )PhiMLP   c                   > [         TU ]  5         Xl        [        UR                     U l        [        R                  " UR                  UR                  5      U l
        [        R                  " UR                  UR                  5      U l        g N)rg   rh   r`   r   
hidden_actactivation_fnrQ   rn   rj   intermediate_sizefc1fc2r|   r`   r}   s     r(   rh   PhiMLP.__init__   sb    #F$5$5699V//1I1IJ99V55v7I7IJr*   r6   r8   c                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ r   )r   r   r   )r|   r6   s     r(   r   PhiMLP.forward   s4    /**=9/r*   )r   r`   r   r   )
r   r   r   r   rh   r#   r   r   r   r   r   s   @r(   r   r      s)    KU\\ ell  r*   r   c                     ^  \ rS rSrS\S\4U 4S jjr       SS\R                  S\	\R                     S\	\R                     S\	\\R                        S	\	\   S
\	\   S\	\R                     S\	\\R                  \R                  4      S\\R                  \	\\R                  \R                  4      4   4S jjrSrU =r$ )PhiDecoderLayer   r`   ra   c                   > [         TU ]  5         [        XS9U l        [	        U5      U l        [        R                  " UR                  UR                  S9U l
        [        R                  " UR                  5      U l        g )N)ra   re   )rg   rh   r^   	self_attnr   mlprQ   rw   rj   rx   input_layernormDropoutresid_pdropresid_dropoutr{   s      r(   rh   PhiDecoderLayer.__init__   s[    %fB&>!||F,>,>FDYDYZZZ(:(:;r*   r6   rE   r1   r   output_attentions	use_cacher   r   r8   c	                     Un
U R                  U5      nU R                  " SUUUUUUUUS.U	D6u  pU R                  U5      nU R                  U R                  U5      5      nX-   U
-   nU4nU(       a  X4-  nU$ )N)r6   rE   r1   r   r   r   r   r    )r   r   r   r   )r|   r6   rE   r1   r   r   r   r   r   rH   residualattn_outputsself_attn_weightsfeed_forward_hidden_statesoutputss                  r(   r   PhiDecoderLayer.forward   s     !,,]; +/.. 
+
')%)/) 3
+
 
+
' )),7%)%7%78O%P"$AHL "++Gr*   )r   r   r   r   )NNNFFNN)r   r   r   r   r   rs   rh   r#   r   r   r   r   boolFloatTensorr   r   r   r   s   @r(   r   r      s
   <y <S < 26378<,1$)59KO%||% !.% u//0	%
 !u||!45% $D>% D>% !!1!12% &eELL%,,,F&GH% 
u  (51B1BEDUDU1U+V"WW	X% %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$ )PhiRotaryEmbedding   r`   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)rg   rh   hasattr
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr`   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)r|   r`   devicer   r}   s       r(   rh   PhiRotaryEmbedding.__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   r   r   mpscpuF)device_typeenabledr   r    )rK   )r   floatr:   r"   rU   r   r   r   strr#   autocastrP   r$   r/   r   r0   rK   )
r|   r%   r1   inv_freq_expandedposition_ids_expandedr   freqsembr/   r0   s
             r(   r   PhiRotaryEmbedding.forward  sR    !MM$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   r`   r   r   r   r   r   r   )r   r   r   r   r   rh   r#   no_gradr   r   r   r   r   s   @r(   r   r      s6    /y / /" ]]_<  <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	)
PhiPreTrainedModeli"  r`   modelTr   past_key_values)r6   
attentionsr   N)r   r   r   r   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_outputsr   r   r*   r(   r   r   "  sQ    &*#*+#4"5N!"&("r*   r   c                   0  ^  \ 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
\\   S\\   S\\	R                     S\\   S\4S jj5       5       rSrU =r$ )PhiModeli5  r`   c           	      f  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ sH  n[        X5      PM     sn5      U l        [        US9U l        SU l        [
        R"                  " UR$                  5      U l        [
        R(                  " UR                  UR*                  S9U l        U R/                  5         g s  snf )N)r`   Fr   )rg   rh   pad_token_idpadding_idx
vocab_sizerQ   	Embeddingrj   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   
rotary_embgradient_checkpointingr   
embd_pdropembed_dropoutrw   rx   final_layernorm	post_initr{   s      r(   rh   PhiModel.__init__7  s     !.. ++LL):):F<N<NPTP`P`ammAFvG_G_A`aA`I_V/A`a
 -F;&+#ZZ(9(9:!||F,>,>FDYDYZ 	 bs   D.	input_idsrE   r1   r   inputs_embedsr   r   output_hidden_statesr   rH   r8   c
                     Ub  UOU R                   R                  nUb  UOU R                   R                  nUb  UOU R                   R                  nUS L US L-  (       a  [	        S5      eU R
                  (       a/  U R                  (       a  U(       a  [        R                  S5        Sn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 R%                  U5      nUnU R'                  X5      nU(       a  SOS nU(       a  SOS nU R(                  S U R                   R*                    H7  nU(       a  X4-  nU" U4UUUUUU	US	.U
D6nUS   nU(       d  M.  UUS   4-  nM9     U R-                  U5      nU(       a  X4-  n[/        UU(       a  UOS UUS
9$ )Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.Fr   r   )r   )r`   input_embedsrE   r   r   r1   r   )rE   r1   r   r   r   r   r   )last_hidden_stater   r6   r   )r`   r   r  r   
ValueErrorr  rM   loggerwarning_oncer   r	   get_seq_lengthr#   aranger"   r   r,   r   r  r  r  r  r	  r   )r|   r  rE   r1   r   r  r   r   r  r   rH   past_seen_tokensrZ   r6   r   all_hidden_statesall_self_attnsdecoder_layerlayer_outputss                      r(   r   PhiModel.forwardH  s?    2C1N-TXT_T_TqTq$8$D $++JjJj 	 "+!6IDKK<Q<Q	-t";<YZZ&&4==Yj I  --i8M0*nO!CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L(;;&))+%
 **=9% #oomJ #7BD0d![[)H4;;+H+HIM#!%55!)
*)."3#-$7
 
M *!,M  =#3"55' J* ,,];  !11&+/8Od+%	
 	
r*   )r  r   r	  r  r  r   r  r   )	NNNNNNNNN)r   r   r   r   r   rh   r   r   r   r#   r   r   r   r   r   r   r   r   r   r   r   r   s   @r(   r   r   5  s   y "  151537+/59$(,0/359^
E,,-^
 !.^
 u//0	^

 "%^
   1 12^
 D>^
 $D>^
 'tn^
 !!1!12^
 +,^
 
!^
  ^
r*   r   c                   ~  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jrS rS	 r	\
\         SS
\\R                     S\\R                     S\\R                     S\\   S\\R"                     S\\R                     S\\   S\\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )PhiForCausalLMi  zlm_head.weightlm_headcolwise_repr6   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 )NTrc   )
rg   rh   r   r   r   rQ   rn   rj   r  r
  r   s     r(   rh   PhiForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FTR 	r*   c                     Xl         g r   r   )r|   decoders     r(   set_decoderPhiForCausalLM.set_decoder  s    
r*   c                     U R                   $ r   r%  )r|   s    r(   get_decoderPhiForCausalLM.get_decoder  s    zzr*   r  rE   r1   r   r  labelsr   r   logits_to_keeprH   r8   c
                 ~   U R                   " SUUUUUUUS.U
D6nUR                  n[        U	[        5      (       a  [	        U	* S5      OU	nU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U
D6n[        UUUR                  UR                  UR                  S9$ )ac  
Example:

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

>>> model = PhiForCausalLM.from_pretrained("meta-phi/Phi-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-phi/Phi-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."
```)r  rE   r1   r   r  r   r   N)r!  r,  r   )lossr!  r   r6   r   r   )r   r  r   rs   slicer  loss_functionr`   r   r   r   r6   r   )r|   r  rE   r1   r   r  r,  r   r   r-  rH   r   r6   slice_indicesr!  r/  s                   r(   r   PhiForCausalLM.forward  s    @ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r*   )r  r   r   )	NNNNNNNNr   )r   r   r   r   _tied_weights_keys_tp_plan_pp_planrh   r'  r*  r   r   r   r#   r   r   r   r   r   r   rs   r   r   r   r   r   r   r   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)PhiForSequenceClassificationi  r   Nr   r   r   r   r   r   r*   r(   r8  r8        r*   r8  c                       \ rS rSrSrg)PhiForTokenClassificationi  r   Nr9  r   r*   r(   r<  r<    r:  r*   r<  )r   r   r  r8  r<  )Nr   )r   )=typingr   r   r   r#   torch.nnrQ   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   r   utils.genericr   configuration_phir   
get_loggerr   r  r)   r5   r   rs   r@   Moduler   r\   r^   r   r   r   r   r   r  r8  r<  __all__r   r*   r(   <module>rN     s   - ,   ! . ) / 
 P K F & R R / ( 
		H	%(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4U)299 U)pRYY -0 -`< <D   $ r
! r
 r
j N
' N
 N
b	#CEW 		 =?Q 	r*   