
    hQ                     .   d dl Z 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mZ ddl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$m%Z% ddl&m'Z' ddl(m)Z)  G d dejT                        Z+ G d dejT                        Z, G d dejT                        Z-dej\                  de/dej\                  fdZ0	 d6dejT                  dej\                  dej\                  dej\                  d eej\                     d!e1d"e1d#ee!   fd$Z2d% Z3d7d&Z4 G d' d(ejT                        Z5 G d) d*e      Z6e" G d+ d,e             Z7e" G d- d.e7             Z8e" G d/ d0e7e             Z9 G d1 d2ee7      Z: G d3 d4ee7      Z;g d5Z<y)8    N)CallableOptionalUnion   )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)deprecate_kwarg)check_model_inputs   )HeliumConfigc                   ,     e Zd Zd fd	Zd Zd Z xZS )HeliumRMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y N)super__init__nn	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      h/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/transformers/models/helium/modeling_helium.pyr"   zHeliumRMSNorm.__init__0   s/    ll5::k#:; #    c                 \   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  j                  t        j                        |z  j                  |      S )N   T)keepdim)	dtypetor%   float32powmeanrsqrtr(   r'   )r)   hidden_statesinput_dtypevariances       r-   forwardzHeliumRMSNorm.forward5   s    #))%((7 $$Q',,R,>%Ht?T?T4T(UUu}}-=AA+NNr.   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)tupler'   shaper(   )r)   s    r-   
extra_reprzHeliumRMSNorm.extra_repr<   s*    ))*+6$2G2G1HIIr.   )gư>)__name__
__module____qualname__r"   r<   r@   __classcell__r,   s   @r-   r   r   /   s    $
OJr.   r   c                   ~     e Zd ZU ej                  ed<   ddef fdZ ej                         e	d               Z
 xZS )HeliumRotaryEmbeddinginv_freqconfigc                    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defaultrH   F)
persistent)r!   r"   hasattr
isinstancerK   dictgetrL   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrI   r   rope_init_fnattention_scalingregister_bufferrH   original_inv_freq)r)   rI   devicerH   r,   s       r-   r"   zHeliumRotaryEmbedding.__init__C   s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r.   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   r1   r   mpscpuF)device_typeenabledr0   dim)r3   )rH   floatexpandr?   r4   r[   rQ   rM   strr%   autocast	transposecatcosrX   sinr3   )
r)   xposition_idsinv_freq_expandedposition_ids_expandedr_   freqsembri   rj   s
             r-   r<   zHeliumRotaryEmbedding.forwardT   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    )rA   rB   rC   r%   Tensor__annotations__r   r"   no_gradr   r<   rD   rE   s   @r-   rG   rG   @   s=    ll/| /" U]]_<  <r.   rG   c                   $     e Zd Z fdZd Z xZS )	HeliumMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  |j                        | _        t        j                  | j                  | j                  |j                        | _	        t        j                  | j                  | j                  |j                        | _
        t        |j                     | _        y )Nbias)r!   r"   rI   r*   intermediate_sizer#   Linearmlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr)   rI   r,   s     r-   r"   zHeliumMLP.__init__e   s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r.   c                     | j                  | j                  | j                  |            | j                  |      z        }|S r    )r~   r   r|   r}   )r)   rk   r~   s      r-   r<   zHeliumMLP.forwardo   s6    NN4;;t~~a/@#ADLLQRO#ST	r.   )rA   rB   rC   r"   r<   rD   rE   s   @r-   ru   ru   d   s    0r.   ru   r9   n_repreturnc                     | 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)r?   rd   reshape)r9   r   batchnum_key_value_headsslenhead_dims         r-   	repeat_kvr   t   so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr.   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 )Nr0   r   r1   )rb   r3   )ptrainingr   )r   num_key_value_groupsr%   matmulrg   r?   r#   
functionalsoftmaxr5   r4   r3   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r-   eager_attention_forwardr      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$$r.   c                 |    | ddddf   }| ddddf   }t        j                  | |fd      j                  d      S )	z*Rotates half the hidden dims of the input..r   Nr0   r   r1   ra   r   )r%   stackflatten)rk   x1x2s      r-   rotate_halfr      sJ    	
319B	
319B;;Ryb)11"55r.   c                 F   |j                  |      }|j                  |      }|dd|j                  d   dz  f   j                  dd      }|dd|j                  d   dz  f   j                  dd      }| |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.
        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.
    .Nr1   r0   ra   )	unsqueezer?   repeat_interleaver   )qkri   rj   rl   unsqueeze_dimq_embedk_embeds           r-   apply_rotary_pos_embr      s    ( --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
EC3w;q>C/0G3w;q>C/0GGr.   c                   2    e Zd ZdZddedee   f fdZ eddd      	 	 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	j                  f   fd       Z xZS )HeliumAttentionz=Multi-headed attention from 'Attention Is All You Need' paperrI   	layer_idxc                 \   t         |           || _        || _        t	        |d|j
                  |j                  z        | _        |j                  |j                  z  | _	        dt        j                  | j                        z  | _        |j                  | _        d| _        t        j                   |j
                  |j                  | j                  z  |j"                        | _        t        j                   |j
                  |j                  | j                  z  |j"                        | _        t        j                   |j
                  |j                  | j                  z  |j"                        | _        t        j                   |j
                  |j
                  d      | _        y )Nr   r   Trw   F)r!   r"   rI   r   getattrr*   num_attention_headsr   r   r   mathsqrtr   attention_dropout	is_causalr#   rz   attention_biasq_projk_projv_projo_projr)   rI   r   r,   s      r-   r"   zHeliumAttention.__init__   sC   "
F4F4F&JdJd4de$*$>$>&B\B\$\!499T]]33!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii 2 2F4F4FUSr.   past_key_valuepast_key_values4.58new_nameversionr9   position_embeddingsr   cache_positionr   r   c                 4   |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      j                  |      j	                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  rt        | j                  j                     } || |	|
||f| j                  sdn| j                  | j                   d|\  }} |j"                  g |d j%                         }| j'                  |      }||fS )Nr1   r   r0   )rj   ri   r   eager        )r   r   )r?   r   r   viewrg   r   r   r   updater   r   rI   _attn_implementationr   r   r   r   r   r   r   )r)   r9   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   ri   rj   cache_kwargsattention_interfacer   r   s                     r-   r<   zHeliumAttention.forward   s    $))#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&#&snUL'6'='=j,X\XfXfht'u$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r.   r    )NN)rA   rB   rC   __doc__r   r   intr"   r   r%   rq   r>   r   
LongTensorr   r   r<   rD   rE   s   @r-   r   r      s    GT| T T* %0A6R ,059))||)) #5<<#=>)) !.	))
 "%)) !!1!12)) +,)) 
u||U\\)	*)) S))r.   r   c                   F    e Zd Zddedee   f fdZ eddd      	 	 	 	 	 	 ddej                  d	eej                     d
eej                     dee   dee   deej                     deeej                  ej                  f      dee   dej                  fd       Z xZS )HeliumDecoderLayerrI   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)rI   r   r+   )r!   r"   r*   r   	self_attnru   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r-   r"   zHeliumDecoderLayer.__init__  sl    !--()LV$,V-?-?VEXEXY(5f6H6HfNaNa(b%r.   r   r   r   r   r9   r   rl   	use_cacher   r   r   r   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)r9   r   rl   r   r   r   r    )r   r   r   r   )r)   r9   r   rl   r   r   r   r   r   residual_s              r-   r<   zHeliumDecoderLayer.forward  s     !,,];)4>> 	
')%+) 3	
 	
q !=0 !55mD/ =0r.   r    )NNNFNN)rA   rB   rC   r   r   r   r"   r   r%   rq   r   r   boolr>   r   r   r<   rD   rE   s   @r-   r   r     s    c| c c %0A6R 2637+/$)59KO|| !. u//0	
 "% D> !!1!12 &eELL%,,,F&GH +, 
 Sr.   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)HeliumPreTrainedModelrI   modelTr   r   )r9   
attentionsN)rA   rB   rC   r   rr   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   5  sQ    &*#-.#4"5N!"&+%r.   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j                     d	ee   d
ee   defd              Z xZS )HeliumModelrI   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   F)r!   r"   pad_token_idpadding_idx
vocab_sizer#   	Embeddingr*   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normrG   
rotary_embgradient_checkpointing	post_initr   s      r-   r"   zHeliumModel.__init__J  s     !.. ++LL):):F<N<NPTP`P`ammDI&JbJbDcdy	2d
 "&"4"4&:M:MN	/7&+# 	 es   D 	input_idsr   rl   r   inputs_embedsr   r   r   r   c           
      B   |d u |d uz  rt        d      || j                  |      }|r|t        | j                        }|F||j	                         nd}	t        j                  |	|	|j                  d   z   |j                        }||j                  d      }t        | j                  |||||      }
|}| j                  ||      }| j                  d | j                  j                   D ]  } ||f|
||||d|} | j                  |      }t        ||      S )	Nz:You must specify exactly one of input_ids or inputs_embeds)rI   r   r   )r[   )rI   input_embedsr   r   r   rl   )r   rl   r   r   r   )last_hidden_stater   )
ValueErrorr   r	   rI   get_seq_lengthr%   aranger?   r[   r   r   r  r   r   r  r   )r)   r  r   rl   r   r  r   r   r   past_seen_tokensr   r9   r   decoder_layers                 r-   r<   zHeliumModel.forwardZ  s[    -t";<YZZ *.*;*;I*FM0*$++>O!CRC^==?de+0<< "2]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oom\J![[)H4;;+H+HI 		M)*) /-$7 M		 		-0&++
 	
r.   )NNNNNNN)rA   rB   rC   r   r"   r   r   r   r%   r   rq   r   FloatTensorr   r   r   r   r<   rD   rE   s   @r-   r   r   H  s    |    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r.   r   c                   d    e Zd ZdgZddiZddgdgfiZ 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
j                     de	e   de	e
j                     deee
j                  f   dee   defd              Z xZS )HeliumForCausalLMzlm_head.weightlm_headcolwise_repr9   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFrw   )
r!   r"   r   r   r   r#   rz   r*   r  r  r   s     r-   r"   zHeliumForCausalLM.__init__  sU      (
 ++yy!3!3V5F5FUS 	r.   r  r   rl   r   r  labelsr   r   logits_to_keepr   r   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, HeliumForCausalLM

        >>> model = HeliumForCausalLM.from_pretrained("google/helium-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/helium-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```)r  r   rl   r   r  r   r   N)r  r  r   )lossr  r   r9   r   r   )r   r	  rQ   r   slicer  loss_functionrI   r   r   r   r9   r   )r)   r  r   rl   r   r  r  r   r   r  r   outputsr9   slice_indicesr  r  s                   r-   r<   zHeliumForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r.   )	NNNNNNNNr   )rA   rB   rC   _tied_weights_keys_tp_plan_pp_planr"   r   r   r   r%   r   rq   r   r  r   r   r   r   r   r   r<   rD   rE   s   @r-   r  r    s0   *+=)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                       e Zd Zy)HeliumForSequenceClassificationNrA   rB   rC   r   r.   r-   r"  r"        r.   r"  c                       e Zd Zy)HeliumForTokenClassificationNr#  r   r.   r-   r&  r&    r$  r.   r&  )r   r   r  r"  r&  )r   )Nr   )=r   typingr   r   r   r%   torch.nnr#   activationsr   cache_utilsr   r	   
generationr
   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.deprecationr   utils.genericr   configuration_heliumr   Moduler   rG   ru   rq   r   r   rc   r   r   r   r   r   r   r   r  r"  r&  __all__r   r.   r-   <module>r8     s  ,  , ,   ! . ) / 
 P K F & I I 0 / .JBII J"!<BII !<H		  	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%46BB)bii B)J+3 +\ O  $ K
' K
 K
\ H
- H
 H
V	&FH] 		#@BW 	r.   