
    iU                        d dl mZ d dlmZ d dl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 ddlmZ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$m%Z% ddl&m'Z'm(Z(m)Z) ddl*m+Z+m,Z, ddl-m.Z. ddl/m0Z0  G d dejb                        Z2 G d dejb                        Z3d Z4 ed      d>d       Z5dejl                  de7dejl                  fd Z8	 d?d!ejb                  d"ejl                  d#ejl                  d$ejl                  d%ejl                  dz  d&e9d'e9d(e%e'   fd)Z: ee5       G d* d+ejb                               Z; ed,       G d- d.ejb                               Z< G d/ d0e      Z=e( G d1 d2e#             Z>e( G d3 d4e>             Z?e( G d5 d6e>e             Z@ G d7 d8ee>      ZA G d9 d:ee>      ZB G d; d<ee>      ZCg d=ZDy)@    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)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)maybe_autocastmerge_with_config_defaults)capture_outputs   )Qwen2Configc                   $     e Zd Z fdZd Z xZS )Qwen2MLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFbias)super__init__confighidden_sizeintermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnselfr,   	__class__s     y/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/qwen2/modeling_qwen2.pyr+   zQwen2MLP.__init__$   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../    c                     | j                  | j                  | j                  |            | j                  |      z        }|S N)r2   r4   r0   r1   )r6   xr2   s      r8   forwardzQwen2MLP.forward.   s6    NN4;;t~~a/@#ADLLQRO#ST	r9   )__name__
__module____qualname__r+   r=   __classcell__r7   s   @r8   r%   r%   #   s    0r9   r%   c                        e Zd ZU ej                  ed<   ddef fdZe	 	 	 ddedz  de	d   de
dz  ded	ef   fd
       Z ej                         ed               Z xZS )Qwen2RotaryEmbeddinginv_freqNr,   c                    t         |           |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |      \  }| _
        | j                  d|d       | j                  d|j                         d       y )N	rope_typedefaultrE   F)
persistentoriginal_inv_freq)r*   r+   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr,   rope_parametersrG   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r6   r,   devicerope_init_fnrE   r7   s        r8   r+   zQwen2RotaryEmbedding.__init__6   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuUr9   rS   ztorch.deviceseq_lenreturnztorch.Tensorc                    | j                   d   }t        | dd      xs | j                  | j                  z  }d}d|t	        j
                  d|dt        j                        j                  |t        j                        |z  z  z  }||fS )	a  
        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head_dimNg      ?r      dtype)rS   r\   )	rN   getattrr-   num_attention_headstorcharangeint64tofloat)r,   rS   rU   basedimattention_factorrE   s          r8   rO   z4Qwen2RotaryEmbedding.compute_default_rope_parametersF   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r9   c                 N   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r"   mpscpuF)device_typeenabledrZ   re   r[   )rE   rc   expandshaperb   rS   
isinstancetypestrr   	transposer_   catcosrP   sinr\   )
r6   r<   position_idsinv_freq_expandedposition_ids_expandedrk   freqsembru   rv   s
             r8   r=   zQwen2RotaryEmbedding.forwardd   sR    !MM$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;   )NNN)r>   r?   r@   r_   Tensor__annotations__r#   r+   staticmethodr   inttuplerc   rO   no_gradr   r=   rA   rB   s   @r8   rD   rD   3   s    llV{ V  %)+/"*d"*(* t* 
~u$	%	* *: U]]_<  <r9   rD   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..Nrh   rZ   rm   )ro   r_   rt   )r<   x1x2s      r8   rotate_halfr   t   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r9   rotary_pos_embc                     |j                  |      }|j                  |      }| |z  t        |       |z  z   }||z  t        |      |z  z   }||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.
        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kru   rv   unsqueeze_dimq_embedk_embeds          r8   apply_rotary_pos_embr   {   sY    & --
&C
--
&C3w;q>C/0G3w;q>C/0GGr9   hidden_statesn_reprV   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)ro   rn   reshape)r   r   batchnum_key_value_headsslenrY   s         r8   	repeat_kvr      so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr9   modulequerykeyvalueattention_maskscalingdropoutkwargsc                    t        || j                        }t        || j                        }	t        j                  ||j	                  dd            |z  }
||
|z   }
t
        j                  j                  |
dt        j                        j                  |j                        }
t
        j                  j                  |
|| j                        }
t        j                  |
|	      }|j	                  dd      j                         }||
fS )NrZ   r   rh   )re   r\   )ptrainingr"   )r   num_key_value_groupsr_   matmulrs   r   
functionalsoftmaxfloat32rb   r\   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r8   eager_attention_forwardr      s     3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!#n4==((2U]](SVVW\WbWbcL==((6??([L,,|\:K''1-88:K$$r9   c                        e Zd ZdZdedef fdZ	 ddej                  de	ej                  ej                  f   dej                  dz  d	e
dz  d
ee   de	ej                  ej                  dz  f   fdZ xZS )Qwen2Attentionz=Multi-headed attention from 'Attention Is All You Need' paperr,   	layer_idxc                    t         |           t        |d      r|j                  |   nd | _        || _        || _        t        |d|j                  |j                  z        | _
        |j                  |j                  z  | _        | j                  dz  | _        |j                  | _        d| _        t!        j"                  |j                  |j                  | j                  z  d      | _        t!        j"                  |j                  |j                  | j                  z  d      | _        t!        j"                  |j                  |j                  | j                  z  d      | _        t!        j"                  |j                  | j                  z  |j                  d      | _        | j                  dk(  r|j,                  | _        y d | _        y )Nlayer_typesrY   g      Tr(   Fsliding_attention)r*   r+   hasattrr   
layer_typer,   r   r]   r-   r^   rY   r   r   r   attention_dropout	is_causalr   r/   q_projk_projv_projo_projsliding_windowr6   r,   r   r7   s      r8   r+   zQwen2Attention.__init__   sl   ;B6=;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijii 2 2F4N4NQUQ^Q^4^eijii : :T]] JFL^L^ejk7;J]7]f33cgr9   Nr   position_embeddingsr   past_key_valuesr   rV   c                     |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
|\  }}t        ||	||      \  }}	| |j                  |	|
| j                        \  }	}
t        j                  | j                  j                  t              } || ||	|
|f| j                  sdn| j                   | j"                  | j$                  d|\  }} |j&                  g |d j)                         }| j+                  |      }||fS )Nrh   r"   rZ           )r   r   r   )ro   rY   r   viewrs   r   r   r   updater   r   get_interfacer,   _attn_implementationr   r   r   r   r   r   r   r   )r6   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   ru   rv   attention_interfacer   r   s                   r8   r=   zQwen2Attention.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&S#7jRUWZ#[ j&'6'='=j,X\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
!\ *k));;;;FFHkk+.L((r9   r;   )r>   r?   r@   __doc__r#   r   r+   r_   r|   r   r   r   r   r=   rA   rB   s   @r8   r   r      s    Gh{ hs h* )-')||') #5<<#=>') t+	')
 ') -.') 
u||U\\D00	1')r9   r   RMSNormc                   h     e Zd Zddeddf fdZdej                  dej                  fdZd Z xZ	S )	Qwen2RMSNormepsrV   Nc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z;
        Qwen2RMSNorm is equivalent to T5LayerNorm
        N)r*   r+   r   	Parameterr_   onesweightvariance_epsilon)r6   r-   r   r7   s      r8   r+   zQwen2RMSNorm.__init__   s1     	ll5::k#:; #r9   r   c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )NrZ   rh   T)keepdim)	r\   rb   r_   r   powmeanrsqrtr   r   )r6   r   input_dtypevariances       r8   r=   zQwen2RMSNorm.forward  sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r9   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)r   r   ro   r   )r6   s    r8   
extra_reprzQwen2RMSNorm.extra_repr	  s*    ))*+6$2G2G1HIIr9   )gư>)
r>   r?   r@   rc   r+   r_   r|   r=   r   rA   rB   s   @r8   r   r      s7    $ $$ $;U\\ ;ell ;Jr9   r   c                       e Zd Zdedef fdZ	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   dej                  fdZ xZS )Qwen2DecoderLayerr,   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)r,   r   r   )r*   r+   r-   r   	self_attnr%   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r8   r+   zQwen2DecoderLayer.__init__  sl    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r9   Nr   r   rw   r   	use_cacher   r   rV   c           
          |}| j                  |      } | j                  d||||||d|\  }}	||z   }|}| j                  |      }| j                  |      }||z   }|S )N)r   r   rw   r   r   r    )r   r   r   r   )
r6   r   r   rw   r   r   r   r   residual_s
             r8   r=   zQwen2DecoderLayer.forward  s     !,,];)4>> 
')%+ 3
 
q !=0 !55mD/ =0r9   )NNNFN)r>   r?   r@   r#   r   r+   r_   r|   
LongTensorr   boolr   r   r   r=   rA   rB   s   @r8   r   r     s    b{ bs b /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
r9   r   c                   J    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y)Qwen2PreTrainedModelr,   modelTr   r   )r   
attentionsN)r>   r?   r@   r#   r}   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   r9   r8   r   r   8  sQ    &*#,-#4"5N!"&*$r9   r   c                        e Zd Zdef fdZeee	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  dedz  dej                  dz  d	edz  d
ee   defd                     Z xZS )
Qwen2Modelr,   c           	      F   t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        d| j(                  j*                  v | _        | j/                          y c c}w )Nr   r,   Fr   )r*   r+   pad_token_idpadding_idx
vocab_sizer   	Embeddingr-   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normrD   
rotary_embgradient_checkpointingr,   r   has_sliding_layers	post_initr   s      r8   r+   zQwen2Model.__init__M  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcivy1c
 !!3!39L9LM	.f=&+#"59P9P"P 	 ds   DN	input_idsr   rw   r   inputs_embedsr   r   rV   c           
         |d u |d uz  rt        d      || j                  |      }|r|t        | j                        }|V||j	                         nd}t        j                  |j                  d   |j                        |z   }|j                  d      }t        |x}	t              s9| j                  ||||d}
dt        di |
i}	| j                  rt        di |
|	d<   |}| j                  ||      }t!        | j"                  d | j                  j$                         D ].  \  }} ||f|	| j                  j&                  |      ||||d	|}0 | j)                  |      }t+        ||r|
      S d 
      S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r"   )rS   )r,   r  r   r   rw   full_attentionr   )r   r   rw   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r,   get_seq_lengthr_   r`   ro   rS   r   rp   dictr   r
  r   r  	enumerater  r  r   r  r   )r6   r  r   rw   r   r  r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr   r   idecoder_layers                  r8   r=   zQwen2Model.forward^  s    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L ?-F ++!."0#2 ,K !"4"C{"C# &&;\;k_j;k#$78%"oom\J )$++6U8U8U*V W 		A})24;;3J3J13MN$7) /# M		 		-0&+/8O
 	
>B
 	
r9   )NNNNNN)r>   r?   r@   r#   r+   r    r!   r   r_   r   r|   r   FloatTensorr   r   r   r   r=   rA   rB   s   @r8   r   r   K  s    { "   .2.204(,26!%<
##d*<
 t+<
 &&-	<

 <
 ((4/<
 $;<
 +,<
 
!<
    <
r9   r   c                   B    e Zd ZddiZddiZddgdgfiZ fdZee	 	 	 	 	 	 	 	 dd	e	j                  dz  d
e	j                  dz  de	j                  dz  dedz  de	j                  dz  de	j                  dz  dedz  dee	j                  z  dee   defd              Z xZS )Qwen2ForCausalLMzlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y r'   )
r*   r+   r   r   r   r   r/   r-   r  r  r5   s     r8   r+   zQwen2ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r9   Nr  r   rw   r   r  labelsr   logits_to_keepr   rV   c	           
      x    | 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, Qwen2ForCausalLM

        >>> model = Qwen2ForCausalLM.from_pretrained("meta-qwen2/Qwen2-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-qwen2/Qwen2-2-7b-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   rw   r   r  r   N)r  r!  r   )lossr  r   r   r   r   )r   r  rp   r   slicer  loss_functionr,   r   r   r   r   r   )r6   r  r   rw   r   r  r!  r   r"  r   outputsr   slice_indicesr  r$  s                  r8   r=   zQwen2ForCausalLM.forward  s    > ,64:: ,
)%+',
 ,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r9   )NNNNNNNr   )r>   r?   r@   _tied_weights_keys_tp_plan_pp_planr+   r   r   r_   r   r|   r   r  r   r   r   r   r   r=   rA   rB   s   @r8   r  r    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r9   r  c                       e Zd Zy)Qwen2ForSequenceClassificationNr>   r?   r@   r   r9   r8   r-  r-        r9   r-  c                       e Zd Zy)Qwen2ForTokenClassificationNr.  r   r9   r8   r1  r1    r/  r9   r1  c                       e Zd ZdZy)Qwen2ForQuestionAnsweringtransformerN)r>   r?   r@   r   r   r9   r8   r3  r3    s    %r9   r3  )r   r   r  r   r-  r1  r3  )r"   )r   )Ecollections.abcr   typingr   r_   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r    utils.output_capturingr!   configuration_qwen2r#   Moduler%   rD   r   r   r|   r   r   rc   r   r   r   r   r   r   r  r-  r1  r3  __all__r   r9   r8   <module>rH     s   %    ! . ) f f R B  P K F & I I G 5 ,ryy  ><299 ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*:)RYY :) +:)z Y'J299 J (J((2 (V ?  $ Q
% Q
 Q
h F
+_ F
 F
R	%EG[ 		"?AU 	& ;=Q &r9   