
    Z jqQ                     H   S SK Jr  S SK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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$r$  SSK%J&r&J'r'J(r(  SSK)J*r*  SSK+J,r,   " S S\RZ                  5      r.S r/\" S5      S7S j5       r0S\Rb                  S\2S\Rb                  4S jr3 S8S\RZ                  S\Rb                  S\Rb                  S \Rb                  S!\Rb                  S-  S"\4S#\4S$\!\#   4S% jjr5\" \05       " S& S'\RZ                  5      5       r6 " S( S)\RZ                  5      r7 " S* S+\5      r8\$ " S, S-\5      5       r9\$ " S. S/\95      5       r:\$ " S0 S1\9\5      5       r; " S2 S3\\95      r< " S4 S5\\95      r=/ S6Qr>g)9    )Callable)OptionalN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_func_from_hubuse_kernelized_func)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstring)can_return_tuplemaybe_autocastmerge_with_config_defaults)capture_outputs   )	PhiConfigc                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )PhiRotaryEmbedding!   inv_freqNconfigc                   > [         TU ]  5         UR                  U l        UR                  U l        Xl        U R
                  R                  S   U l        U R                  nU R                  S:w  a  [        U R                     nU" U R
                  U5      u  o@l
        U R                  SUSS9  U R                  SUR                  5       SS9  g )N	rope_typedefaultr"   F)
persistentoriginal_inv_freq)super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr#   rope_parametersr%   compute_default_rope_parametersr   attention_scalingregister_bufferclone)selfr#   devicerope_init_fnr"   	__class__s        u/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/phi/modeling_phi.pyr*   PhiRotaryEmbedding.__init__$   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuU    r4   ztorch.deviceseq_lenreturnztorch.Tensorc           	      j   U R                   S   nU R                   R                  SS5      n[        U SS5      =(       d    U R                  U R                  -  n[        XT-  5      nSnSU[        R                  " SUS[        R                  S9R                  U[        R                  S	9U-  -  -  nX4$ )
aH  
Computes the inverse frequencies according to the original RoPE implementation
Args:
    config ([`~transformers.PreTrainedConfig`]):
        The model configuration.
    device (`torch.device`):
        The device to use for initialization of the inverse frequencies.
    seq_len (`int`, *optional*):
        The current sequence length. Unused for this type of RoPE.
Returns:
    Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
    post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).

rope_thetapartial_rotary_factorg      ?head_dimNr      dtype)r4   rB   )r.   getgetattrhidden_sizenum_attention_headsinttorcharangeint64tofloat)	r#   r4   r:   baser>   r?   dimattention_factorr"   s	            r7   r/   2PhiRotaryEmbedding.compute_default_rope_parameters4   s    & %%l3 & 6 6 : :;RTW X6:t4h8J8JfNhNh8h(23 U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r9   c                 L   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[        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$ ! , (       d  f       N@= f)Nr   r   mpscpuF)device_typeenabledr@   rN   rA   )r"   rL   expandshaperK   r4   
isinstancetypestrr   	transposerH   catcosr0   sinrB   )
r3   xposition_idsinv_freq_expandedposition_ids_expandedrU   freqsembr_   r`   s
             r7   forwardPhiRotaryEmbedding.forwardT   sN    !MM$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$cff177f&;;; DCs   BF
F#)r0   r#   r,   r-   r%   N)NNN)__name__
__module____qualname____firstlineno__rH   Tensor__annotations__r   r*   staticmethodr   rG   tuplerL   r/   no_gradr   rg   __static_attributes____classcell__r6   s   @r7   r    r    !   s    llVy V V  #'+/"*D *(* t* 
~u$	%	* *> ]]_<  <r9   r    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$ )z*Rotates half the hidden dims of the input..NrR   r@   rW   )rY   rH   r^   )ra   x1x2s      r7   rotate_halfry   d   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r9   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )aI  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.
    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.
)	unsqueezery   )qkr_   r`   unsqueeze_dimq_embedk_embeds          r7   apply_rotary_pos_embr   k   sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr9   hidden_statesn_repr;   c                     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)rY   rX   reshape)r   r   batchnum_key_value_headsslenr?   s         r7   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr9   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  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$ )Nr@   r   rR   )rN   rB   )ptrainingr   )r   num_key_value_groupsrH   matmulr]   nn
functionalsoftmaxfloat32rK   rB   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r7   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r9   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
\S-  S\
\R                  \R                  S-  4   4
S jjrSrU =r$ )PhiAttention   z=Multi-headed attention from 'Attention Is All You Need' paperr#   	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(                  S   -  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biasr>   )epselementwise_affine)r)   r*   r#   r   rD   rE   rF   r?   r   r   r   attention_dropout	is_causalr   Linearq_projk_projv_projdenserG   r.   rotary_ndimsqk_layernorm	LayerNormlayer_norm_epsq_layernormk_layernormr3   r#   r   r6   s      r7   r*   PhiAttention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijYYv99DMMI6K]K]dhi
0F0FG^0_ _`"//!||""f&@&@@fF[F[pt D  "||""f&@&@@fF[F[pt D	 r9   Nr   position_embeddingsr   past_key_valuesr;   c                 b   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  (       a"  U R                  U5      nU R                  U	5      n	Uu  pUSS U R                  24   USU R                  S 24   pU	SS U R                  24   U	SU R                  S 24   nn[        XX5      u  p[        R                  " X4SS9n[        R                  " UU4SS9n	Ub  UR                  XU R                  5      u  p[         R"                  " U R$                  R&                  [(        5      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R0                  " / UQSP76 R3                  5       nU R5                  U5      nUU4$ )NrR   r   r@   .rW           )r   r   )rY   r?   r   viewr]   r   r   r   r   r   r   r   rH   r^   updater   r   get_interfacer#   _attn_implementationr   r   r   r   r   r   r   )r3   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   r_   r`   	query_rot
query_passkey_rotkey_passattention_interfacer   r   s                       r7   rg   PhiAttention.forward   sD    $))#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)cO	 yy)!8bAYY2;
&'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHjj-L((r9   )r   r#   r   r?   r   r   r   r   r   r   r   r   r   r   r   ri   )rj   rk   rl   rm   __doc__r   rG   r*   rH   rn   rq   r   rg   rs   rt   ru   s   @r7   r   r      s    Gy S 8 )-8)||8) #5<<#=>8) t+	8)
 8) 
u||U\\D00	18) 8)r9   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 ri   )r)   r*   r#   r   
hidden_actactivation_fnr   r   rE   intermediate_sizefc1fc2r3   r#   r6   s     r7   r*   PhiMLP.__init__  sb    #F$5$5699V//1I1IJ99V55v7I7IJr9   r   r;   c                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ ri   )r   r   r   )r3   r   s     r7   rg   PhiMLP.forward  s4    /**=9/r9   )r   r#   r   r   )
rj   rk   rl   rm   r*   rH   rn   rg   rs   rt   ru   s   @r7   r   r      s)    KU\\ ell  r9   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )PhiDecoderLayeri  r#   r   c                   > [         TU ]  5         [        XS9U l        [	        U5      U l        [        R                  " UR                  UR                  S9U l
        [        R                  " UR                  5      U l        g )N)r   r   )r)   r*   r   	self_attnr   mlpr   r   rE   r   input_layernormDropoutresid_pdropresid_dropoutr   s      r7   r*   PhiDecoderLayer.__init__  s[    %fB&>!||F,>,>FDYDYZZZ(:(:;r9   Nr   r   rb   r   	use_cacher   r   r;   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pU R                  U	5      n	U R                  U R                  U5      5      nX-   U-   nU$ )N)r   r   rb   r   r   r    )r   r   r   r   )r3   r   r   rb   r   r   r   r   residualattn_outputs_feed_forward_hidden_statess               r7   rg   PhiDecoderLayer.forward  s     !,,];.. 
')%+ 3
 
 )),7%)%7%78O%P"$AHLr9   )r   r   r   r   )NNNFN)rj   rk   rl   rm   r   rG   r*   rH   rn   
LongTensorr   boolrq   r   r   rg   rs   rt   ru   s   @r7   r   r     s    <y <S < /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r9   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	)
PhiPreTrainedModeli6  r#   modelTr   r   )r   
attentionsr   N)rj   rk   rl   rm   r   ro   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_outputsrs   r   r9   r7   r   r   6  sQ    &*#*+#4"5N!"&("r9   r   c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )PhiModeliI  r#   c           	      h  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        US9U l        SU l        [
        R"                  " UR$                  5      U l        [
        R(                  " UR                  UR*                  S9U l        U R/                  5         g s  snf )Nr#   Fr   )r)   r*   pad_token_idpadding_idx
vocab_sizer   	EmbeddingrE   embed_tokens
ModuleListrangenum_hidden_layersr   layersr    
rotary_embgradient_checkpointingr   
embd_pdropembed_dropoutr   r   final_layernorm	post_initr   s      r7   r*   PhiModel.__init__K  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/N	input_idsr   rb   r   inputs_embedsr   r   r;   c           
      `   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	U R                  U5      nUn
U R                  XS9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$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r   )r4   )r#   r  r   r   rb   )rb   )r   rb   r   r   r   )last_hidden_stater   )
ValueErrorr  r   r#   get_seq_lengthrH   rI   rY   r4   r|   r   r  r	  r  r  r  r   )r3   r  r   rb   r   r  r   r   past_seen_tokenscausal_maskr   r   decoder_layers                r7   rg   PhiModel.forward\  sX    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 **=9%"oomoW![[)H4;;+H+HIM)*) /#$7 M J ,,];&++
 	
r9   )r  r  r  r
  r  r  r	  r  )NNNNNN)rj   rk   rl   rm   r   r*   r   r   r   rH   r   rn   r   FloatTensorr   r   r   r   rg   rs   rt   ru   s   @r7   r   r   I  s    y "   .2.204(,26!%4
##d*4
 t+4
 &&-	4

 4
 ((4/4
 $;4
 +,4
 
!4
    4
r9   r   c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )PhiForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr   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 )NTr   )
r)   r*   r   r   r  r   r   rE   r  r  r   s     r7   r*   PhiForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FTR 	r9   Nr  r   rb   r   r  labelsr   logits_to_keepr   r;   c	           
      |   U R                   " S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  r   rb   r   r  r   N)r  r"  r  )lossr  r   r   r   r   )r   r  rZ   rG   slicer  loss_functionr#   r  r   r   r   r   )r3   r  r   rb   r   r  r"  r   r#  r   outputsr   slice_indicesr  r%  s                  r7   rg   PhiForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r9   )r  r   r  )NNNNNNNr   )rj   rk   rl   rm   _tied_weights_keys_tp_plan_pp_planr*   r   r   rH   r   rn   r   r  r   rG   r   r   r   rg   rs   rt   ru   s   @r7   r  r    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r9   r  c                       \ rS rSrSrg)PhiForSequenceClassificationi  r   Nrj   rk   rl   rm   rs   r   r9   r7   r/  r/        r9   r/  c                       \ rS rSrSrg)PhiForTokenClassificationi  r   Nr0  r   r9   r7   r3  r3    r1  r9   r3  )r   r   r  r/  r3  )r   )r   )?collections.abcr   typingr   rH   torch.nnr   activationsr   cache_utilsr   r   
generationr	   integrationsr
   r   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.genericr   r   r   utils.output_capturingr   configuration_phir   Moduler    ry   r   rn   rG   r   rL   r   r   r   r   r   r   r  r/  r3  __all__r   r9   r7   <module>rG     s   %    ! . ) I / 
 P K F & 7 Y Y 5 (@< @<F( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*R)299 R) +R)jRYY $0 $N   $ I
! I
 I
X F
' F
 F
R	#CEW 		 =?Q 	r9   