
    rh5Z                     L   d dl mZmZmZ d dlZd dlmZ d dlmZ ddlm	Z	 ddl
mZmZ ddlmZ dd	lmZ dd
lmZmZ ddlmZ ddlmZmZmZ ddlmZmZ ddlmZmZ ddl m!Z!m"Z" ddl#m$Z$ ddl%m&Z&m'Z'm(Z( ddl)m*Z*  G d dejV                        Z,d Z-dej\                  de/dej\                  fdZ0	 d9dejV                  dej\                  dej\                  dej\                  deej\                     d e1d!e1d"e$e&   fd#Z2d:d$Z3 G d% d&ejV                        Z4 ed'       G d( d)ejV                               Z5 G d* d+e      Z6e' G d, d-e"             Z7 G d. d/ejV                        Z8e' G d0 d1e7             Z9e' G d2 d3e7e             Z: G d4 d5ee7      Z; G d6 d7ee7      Z<g d8Z=y);    )CallableOptionalUnionN)nn)check_model_inputs   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple   )
Phi3Configc                   V     e Zd Z fdZdej
                  dej
                  fdZ xZS )Phi3MLPc                 *   t         |           || _        t        j                  |j
                  d|j                  z  d      | _        t        j                  |j                  |j
                  d      | _        t        |j                     | _        y )N   Fbias)super__init__configr   Linearhidden_sizeintermediate_sizegate_up_proj	down_projr	   
hidden_actactivation_fnselfr(   	__class__s     y/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/phi3/modeling_phi3.pyr'   zPhi3MLP.__init__2   sp    IIf&8&8!f>V>V:V]bc6#;#;V=O=OV[\#F$5$56    hidden_statesreturnc                     | j                  |      }|j                  dd      \  }}|| j                  |      z  }| j                  |      S )Nr#   dim)r,   chunkr/   r-   )r1   r5   	up_statesgates       r3   forwardzPhi3MLP.forward:   sL    %%m4	#//!/4i 2 24 88	~~i((r4   )__name__
__module____qualname__r'   torchFloatTensorr>   __classcell__r2   s   @r3   r!   r!   1   s'    7)U%6%6 )5;L;L )r4   r!   c                     | dd| j                   d   dz  f   }| d| j                   d   dz  df   }t        j                  | |fd      S )z*Rotates half the hidden dims of the input..Nr8   r#   r9   )shaperB   cat)xx1x2s      r3   rotate_halfrL   C   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r4   r5   n_repr6   c                     | j                   \  }}}}|dk(  r| S | dddddddddf   j                  |||||      } | j                  |||z  ||      S )z
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    r   N)rG   expandreshape)r5   rM   batchnum_key_value_headsslenhead_dims         r3   	repeat_kvrU   J   so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr4   modulequerykeyvalueattention_maskscalingdropoutkwargsc                 T   t        || j                        }t        || j                        }	t        j                  ||j	                  dd            |z  }
|#|d d d d d d d |j
                  d   f   }|
|z   }
t        j                  j                  |
dt        j                        j                  |j                        }
t        j                  j                  |
|| j                        }
t        j                  |
|	      }|j	                  dd      j                         }||
fS )Nr#   r   r8   )r:   dtype)ptrainingr   )rU   num_key_value_groupsrB   matmul	transposerG   r   
functionalsoftmaxfloat32tor`   r\   rb   
contiguous)rV   rW   rX   rY   rZ   r[   r\   r]   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r3   eager_attention_forwardrp   V   s    3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#k1==((2U]](SVVW\WbWbcL==((6??([L,,|\:K''1-88:K$$r4   c                 `   |j                  |      }|j                  |      }|j                  d   }| dd|f   | d|df   }}|dd|f   |d|df   }
}	t        j                  ||z  t	        |      |z  z   |gd      }t        j                  |	|z  t	        |	      |z  z   |
gd      }||fS )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.
    r8   .Nr9   )	unsqueezerG   rB   rH   rL   )qkcossinposition_idsunsqueeze_dim
rotary_dimq_rotq_passk_rotk_passq_embedk_embeds                r3   apply_rotary_pos_embr   p   s    ( --
&C
--
&C2Jc;J;&'3
+;)<6Ec;J;&'3
+;)<6Eii%#++e*<s*BCVLRTUGii%#++e*<s*BCVLRTUGGr4   c                   >    e Zd ZdZddedee   f fdZ	 	 ddej                  de
ej                  ej                  f   deej                     dee   d	eej                     d
ee   de
ej                  eej                     ee
ej                        f   fdZ xZS )Phi3Attentionz=Multi-headed attention from 'Attention Is All You Need' paperr(   	layer_idxc                 |   t         |           || _        || _        t	        |d|j
                  |j                  z        | _        |j                  |j                  z  | _	        |j                  | _        | j                  dz  | _
        |j                  | _        d| _        |j                  | j                  z  d|j                  | j                  z  z  z   }t        j                  |j                  | j                  z  |j
                  d      | _        t        j                  |j
                  |d      | _        y )NrT   g      Tr#   Fr$   )r&   r'   r(   r   getattrr*   num_attention_headsrT   rR   rc   r[   attention_dropout	is_causalr   r)   o_projqkv_proj)r1   r(   r   op_sizer2   s       r3   r'   zPhi3Attention.__init__   s    "
F4F4F&JdJd4de$*$>$>&B\B\$\!#)#=#= }}d*!'!9!9,,t}}<qFD^D^aeananDn?ooii : :T]] JFL^L^ejk		&"4"4gEJr4   r5   position_embeddingsrZ   past_key_valuecache_positionr]   r6   c           
         |j                   d d }g |d| j                  }| j                  |      }	| j                  j                  | j                  z  }
|	dd |
f   }|	d|
|
| j
                  | j                  z  z   f   }|	d|
| j
                  | j                  z  z   d f   }|j                  |      j                  dd      }|j                  |      j                  dd      }|j                  |      j                  dd      }|\  }}t        ||||      \  }}|'|||d}|j                  ||| j                  |      \  }}t        }| j                  j                  dk7  rt        | j                  j                     } || ||||f| j                  sdn| j                  | j                   t#        | j                  dd       d	|\  }} |j$                  g |d j'                         }| j)                  |      }||fS )
Nr8   .r   r#   )rv   ru   r   eager        sliding_window)r\   r[   r   )rG   rT   r   r(   r   rR   viewre   r   updater   rp   _attn_implementationr   rb   r   r[   r   rP   rj   r   )r1   r5   r   rZ   r   r   r]   input_shapehidden_shapeqkv	query_posquery_statesrk   rl   ru   rv   cache_kwargsattention_interfacero   rm   s                       r3   r>   zPhi3Attention.forward   s$    $))#2.88b8$--8mmM*KK33dmmC	3

?+i)d6N6NQUQ^Q^6^*^^^_
3	D,D,Dt}},T T VVW#((6@@AF__\2<<QB
#((6@@AF&S#7jRUWZ#[ j%#&snUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7
%
  $}}C$2H2HLL"4;;0@$G
%
 
%
!\ *k));;;;FFHkk+.L((r4   N)NN)r?   r@   rA   __doc__r   r   intr'   rB   Tensortupler
   
LongTensorr   r   r>   rD   rE   s   @r3   r   r      s    GKz Khsm K( +/590)||0) #5<<#=>0) !.	0)
 !0) !!1!120) -.0) 
u||Xell3XeELL>Q5RR	S0)r4   r   RMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )Phi3RMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z:
        Phi3RMSNorm is equivalent to T5LayerNorm
        N)r&   r'   r   	ParameterrB   onesweightvariance_epsilon)r1   r*   epsr2   s      r3   r'   zPhi3RMSNorm.__init__   s1     	ll5::k#:; #r4   c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )Nr#   r8   T)keepdim)	r`   ri   rB   rh   powmeanrsqrtr   r   )r1   r5   input_dtypevariances       r3   r>   zPhi3RMSNorm.forward   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r4   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)r   r   rG   r   r1   s    r3   
extra_reprzPhi3RMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr4   )gư>)r?   r@   rA   r'   r>   r   rD   rE   s   @r3   r   r      s    $;Jr4   r   c                   d    e Zd Zdedef fdZ	 	 	 	 	 	 ddej                  deej                     deej                     dee
   dee   d	eej                     d
eeej                  ej                  f      dee   deej                  eeej                  ej                  f      f   fdZ xZS )Phi3DecoderLayerr(   r   c                    t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        || _        t        j                  |j                        | _        t        j                  |j                        | _        y )N)r(   r   r   )r&   r'   r*   r   	self_attnr!   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr(   r   Dropoutresid_pdropresid_attn_dropoutresid_mlp_dropoutr1   r(   r   r2   s      r3   r'   zPhi3DecoderLayer.__init__   s    !--&f	J6?*6+=+=6CVCVW(3F4F4FFL_L_(`%"$**V-?-?"@!#F,>,>!?r4   r5   rZ   rw   r   	use_cacher   r   r]   r6   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	| j                  |      z   }|}	| j                  |      }| j	                  |      }|	| j                  |      z   }|S )N)r5   rZ   rw   r   r   r   r    )r   r   r   r   r   r   )r1   r5   rZ   rw   r   r   r   r   r]   residualself_attn_weightss              r3   r>   zPhi3DecoderLayer.forward   s     !,,];+94>> 	,
')%)) 3	,
 	,
(( !4#:#:=#II 55mD/ 4#9#9-#HHr4   )NNNFNN)r?   r@   rA   r   r   r'   rB   r   r   r   r
   boolr   r   r   rC   r>   rD   rE   s   @r3   r   r      s    	@z 	@c 	@ 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH -. 
u  (51B1BEDUDU1U+V"WW	Xr4   r   c                   N    e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedZdZy)	Phi3PreTrainedModelr(   modelTr   past_key_values)r5   
attentionsz0.0.5N)r?   r@   rA   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_outputs_versionr   r4   r3   r   r     sX    &*#+,#4"5N!"&)# Hr4   r   c                   ^     e Zd Zddef fdZ ej                         ed               Z xZ	S )Phi3RotaryEmbeddingr(   c                    t         |           t        |d      rUt        |j                  t
              r;|j                  j                  d|j                  j                  d            | _        nd| _        |j                  | _	        |j                  | _
        || _        t        | j                     | _        | j                  | j                  |      \  }| _        | j                  d|d       | j                   | _        y )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_lenr(   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)r1   r(   devicer   r2   s       r3   r'   zPhi3RotaryEmbedding.__init__,  s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r4   c                 b   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        j                  |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r8   r   mpscpuF)device_typeenabledr#   r9   )r`   )r   floatrO   rG   ri   r   r   r   strrB   autocastre   rH   ru   r   rv   r`   )
r1   rI   rw   inv_freq_expandedposition_ids_expandedr   freqsembru   rv   s
             r3   r>   zPhi3RotaryEmbedding.forward=  sV    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk^^UC 	5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C		5 vvAGGv$cff177f&;;;	5 	5s    BF%%F.r   )
r?   r@   rA   r   r'   rB   no_gradr   r>   rD   rE   s   @r3   r   r   +  s3    /z /" U]]_<  <r4   r   c                       e Zd Zdef fdZee	 	 	 	 	 	 	 ddeej                     deej                     deej                     dee   deej                     dee   d	eej                     d
ee   defd              Z xZS )	Phi3Modelr(   c           	         t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   )r(   F)r&   r'   pad_token_idpadding_idx
vocab_sizer   	Embeddingr*   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointing	post_initr   s      r3   r'   zPhi3Model.__init__O  s     !.. ++LL):):F<N<NPTP`P`ammBGH`H`BabYfi0b
   2 28K8KL	-V<&+# 	 cs   D	input_idsrZ   rw   r   inputs_embedsr   r   r]   r6   c                 |   |d u |d uz  rt        d      || j                  |      }|r|
t               }|F||j                         nd}	t	        j
                  |	|	|j                  d   z   |j                        }||j                  d      }| j                  j                  t        nt        }
 |
| j                  |||||      }|}| j                  ||      }| j                  d | j                  j                   D ]  } ||f||||||d|} | j!                  |      }t#        ||r|      S d       S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )r   )r(   input_embedsrZ   r   r   rw   )rZ   rw   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r   get_seq_lengthrB   arangerG   r   rr   r(   r   r   r   r
  r  r  r	  r   )r1   r  rZ   rw   r   r  r   r   r]   past_seen_tokensmask_functionrn   r5   r   decoder_layers                  r3   r>   zPhi3Model.forward_  s~    -t";<YZZ  --i8M0*nO!CRC^==?de"\\ "2]5H5H5K"KTaThThN )33A6L.2kk.H.H.P*Vw#;;&))+%
 &"oom\J![[)H4;;+H+HI 
	M)	*).#-$7	 	M
	 		-0&+/8O
 	
>B
 	
r4   )NNNNNNN)r?   r@   rA   r   r'   r   r   r   rB   r   r   r
   rC   r   r   r   r   r>   rD   rE   s   @r3   r   r   M  s    z    151537+/59$(599
E,,-9
 !.9
 u//0	9

 "%9
   1 129
 D>9
 !!1!129
 +,9
 
!9
  9
r4   r   c                       e Zd ZdgZddiZddgdgfiZ fdZd Zd Ze	e
	 	 	 	 	 	 	 	 	 dd	eej                     d
eej                     deej                     dee   deej                      deej                     dee   deej                     deeej                  f   dee   defd              Z	 	 	 	 	 	 	 d fd	Z xZS )Phi3ForCausalLMzlm_head.weightlm_headcolwise_repr5   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFr$   )
r&   r'   r   r   r  r   r)   r*   r  r  r0   s     r3   r'   zPhi3ForCausalLM.__init__  sU     v&
 ++yy!3!3V5F5FUS 	r4   c                     || _         y r   r   )r1   decoders     r3   set_decoderzPhi3ForCausalLM.set_decoder  s	    
r4   c                     | j                   S r   r  r   s    r3   get_decoderzPhi3ForCausalLM.get_decoder  s    zzr4   r  rZ   rw   r   r  labelsr   r   logits_to_keepr]   r6   c
                 z    | j                   d|||||||d|
}|j                  }t        |	t              rt	        |	 d      n|	}| j                  |dd|ddf         }d}|* | j                  d||| j                  j                  d|
}t        |||j                  |j                  |j                        S )a  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, Phi3ForCausalLM

        >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-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  rZ   rw   r   r  r   r   N)r  r$  r  )lossr  r   r5   r   r   )r   r  r   r   slicer  loss_functionr(   r  r   r   r5   r   )r1   r  rZ   rw   r   r  r$  r   r   r%  r]   outputsr5   slice_indicesr  r'  s                   r3   r>   zPhi3ForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r4   c	                     |r_| j                   j                  rI|j                  d   | j                   j                  dz   k\  r |d   }
|
| j                   j                  k  rd }t	        |   d||||||||d|	}|S )Nr   r   )r  r   rZ   r  r   rw   r   r%  r   )r(   r   rG    original_max_position_embeddingsr&   prepare_inputs_for_generation)r1   r  r   rZ   r  r   rw   r   r%  r]   past_lengthmodel_inputsr2   s               r3   r.  z-Phi3ForCausalLM.prepare_inputs_for_generation  s    $ (("dkk&R&RUV&VV(+KdkkJJJ"&w< 

+)')%)

 

 r4   )	NNNNNNNNr   )NNNNNTN)r?   r@   rA   _tied_weights_keys_tp_plan_pp_planr'   r!  r#  r   r   r   rB   r   r   r
   rC   r   r   r   r   r   r   r>   r.  rD   rE   s   @r3   r  r    s\   *+=)H_-z:;H  151537+/59-1$(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
z % %r4   r  c                       e Zd Zy)Phi3ForSequenceClassificationNr?   r@   rA   r   r4   r3   r5  r5        r4   r5  c                       e Zd Zy)Phi3ForTokenClassificationNr6  r   r4   r3   r9  r9    r7  r4   r9  )r   r   r  r5  r9  )r   )Nr   )>typingr   r   r   rB   r   transformers.utils.genericr   activationsr	   cache_utilsr
   r   
generationr   integrationsr   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   configuration_phi3r   Moduler!   rL   r   r   rU   r   rp   r   r   r   r   r   r   r   r  r5  r9  __all__r   r4   r3   <module>rK     s  . - ,   9 ! . ) 7 R B 
 P K F & I I *)bii )$(	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4@B)BII B)J Y'J")) J (J(*1 *Z /  &<")) <D L
# L
 L
^ u)? u up	$DFY 		!>@S 	r4   