
    h[O                        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 dd
l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# ddl$m%Z% ddl&m'Z' ddl(m)Z)  ed       G d dejT                               Z+dejX                  de-dejX                  fdZ.	 d5dejT                  dejX                  dejX                  dejX                  deejX                     d e/d!e/d"e e   fd#Z0d6d$Z1d% Z2 G d& d'ejT                        Z3 G d( d)ejT                        Z4 G d* d+e      Z5 G d, d-ejT                        Z6e" G d. d/e             Z7e" G d0 d1e7             Z8e" G d2 d3e7e             Z9g d4Z:y)7    )CallableOptionalUnionN)TransformersKwargs   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)auto_docstringcan_return_tuple)deprecate_kwarg)check_model_inputs   )Olmo2ConfigRMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )Olmo2RMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z;
        Olmo2RMSNorm is equivalent to T5LayerNorm
        N)super__init__nn	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      f/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/transformers/models/olmo2/modeling_olmo2.pyr!   zOlmo2RMSNorm.__init__    s1     	ll5::k#:; #    c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |z  j                  |      S )N   T)keepdim)	dtypetor$   float32powmeanrsqrtr'   r&   )r(   hidden_statesinput_dtypevariances       r,   forwardzOlmo2RMSNorm.forward(   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UUm+//<<r-   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)tupler&   shaper'   )r(   s    r,   
extra_reprzOlmo2RMSNorm.extra_repr/   s*    ))*+6$2G2G1HIIr-   )gư>)__name__
__module____qualname__r!   r;   r?   __classcell__r+   s   @r,   r   r      s    $=Jr-   r   r8   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>   expandreshape)r8   rE   batchnum_key_value_headsslenhead_dims         r,   	repeat_kvrN   3   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 )Nr/   r   r0   )dimr2   )ptrainingr   )rN   num_key_value_groupsr$   matmul	transposer>   r"   
functionalsoftmaxr4   r3   r2   rU   r[   
contiguous)rO   rP   rQ   rR   rS   rT   rU   rV   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r,   eager_attention_forwardrg   ?   s    3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!$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                 
   | j                   |j                   }}|j                  |      }|j                  |      }| |z  t        |       |z  z   }||z  t        |      |z  z   }	|j                  |      |	j                  |      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.
    )r2   	unsqueezerotate_halfr3   )
qkcossinposition_idsunsqueeze_dimq_typek_typeq_embedk_embeds
             r,   apply_rotary_pos_embru   Y   s|    ( WWaggFF
--
&C
--
&C3w;q>C/0G3w;q>C/0G::fwzz&111r-   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..Nr0   r/   rY   )r>   r$   cat)xx1x2s      r,   rj   rj   u   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r-   c                   8    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e	j                     f   fd       Z xZS )Olmo2Attentionz=Multi-headed attention from 'Attention Is All You Need' paperconfig	layer_idxc                 ,   t         |           || _        || _        t	        |d|j
                  |j                  z        | _        |j                  |j                  z  | _	        | j                  d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                  z  |j
                  |j                        | _        t)        |j                  | j                  z  |j*                        | _        t)        |j                  | j                  z  |j*                        | _        y )NrM   g      Tbias)r    r!   r~   r   getattrr)   num_attention_headsrM   rK   r\   rT   attention_dropout	is_causalr"   Linearattention_biasq_projk_projv_projo_projr   rms_norm_epsq_normk_normr(   r~   r   r+   s      r,   r!   zOlmo2Attention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 #6#=#=#MvObObc"6#=#=#MvObObcr-   past_key_valuepast_key_values4.58new_nameversionr8   position_embeddingsrS   cache_positionrV   rF   c                 |   |j                   d d }g |d| j                  }| j                  | j                  |            }	| j	                  | j                  |            }
| j                  |      }|	j                  |      j                  dd      }	|
j                  |      j                  dd      }
|j                  |      j                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  rt        | j                  j                     } || |	|
||f| j                   sdn| j"                  | j$                  d|\  }} |j&                  g |d j)                         }| j+                  |      }||fS )Nr0   r   r/   )rn   rm   r   eager        )rU   rT   )r>   rM   r   r   r   r   r   viewr^   ru   updater   rg   r~   _attn_implementationr   r[   r   rT   rI   ra   r   )r(   r8   r   rS   r   r   rV   input_shapehidden_shapequery_statesrb   rc   rm   rn   cache_kwargsattention_interfacerf   rd   s                     r,   r;   zOlmo2Attention.forward   s    $))#2.88b8$--8{{4;;}#=>[[]!;<
{{=1#((6@@AF__\2<<QB
#((6@@AF&S#7jRUWZ#[ j&#&snUL'6'='=j,X\XfXfht'u$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r-   N)NN)r@   rA   rB   __doc__r   r   intr!   r   r$   Tensorr=   r	   
LongTensorr   r   r;   rC   rD   s   @r,   r}   r}   |   s    Gd{ dx} d2 %0A6R ,059-)||-) #5<<#=>-) !.	-)
 "%-) !!1!12-) +,-) 
u||Xell33	4-) S-)r-   r}   c                   $     e Zd Z fdZd Z xZS )Olmo2MLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFr   )r    r!   r~   r)   intermediate_sizer"   r   	gate_projup_proj	down_projr   
hidden_actact_fnr(   r~   r+   s     r,   r!   zOlmo2MLP.__init__   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r-   c                     | j                  | j                  | j                  |            | j                  |      z        }|S r   )r   r   r   r   )r(   ry   r   s      r,   r;   zOlmo2MLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r-   )r@   rA   rB   r!   r;   rC   rD   s   @r,   r   r      s    0r-   r   c                   >    e Zd Zded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 )Olmo2DecoderLayerr~   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)r~   r   r*   )r    r!   r)   r}   	self_attnr   mlpr   r   post_attention_layernormpost_feedforward_layernormr   s      r,   r!   zOlmo2DecoderLayer.__init__   sl    !--'vKF#(4V5G5GVM`M`(a%*6v7I7IvObOb*c'r-   r   r   r   r   r8   rS   ro   	use_cacher   r   rV   rF   c                     |}	 | j                   d|||||||d|\  }}
| j                  |      }|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)r8   rS   ro   r   r   r   r    )r   r   r   r   )r(   r8   rS   ro   r   r   r   r   rV   residual_s              r,   r;   zOlmo2DecoderLayer.forward   s     !)4>> 	
')%+) 3	
 	
q 55mD =0 !/77F =0r-   )NNNFNN)r@   rA   rB   r   r   r!   r   r$   r   r   r   r	   boolr=   r   r   r;   rC   rD   s   @r,   r   r      s    d{ ds d %0A6R 2637+/$)59KO|| !. u//0	
 "% D> !!1!12 &eELL%,,,F&GH +, 
 S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 )Olmo2RotaryEmbeddinginv_freqr~   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defaultr   F)
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)r(   r~   devicer   r+   s       r,   r!   zOlmo2RotaryEmbedding.__init__  s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r-   c                    | 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  }	||	fcd d d        S # 1 sw Y   y xY w)
Nr   r0   r   mpscpuF)device_typeenabledr/   rw   )r   floatrH   r>   r3   r   r   r   strr$   autocastr^   rx   rm   r   rn   )
r(   ry   ro   inv_freq_expandedposition_ids_expandedr   freqsembrm   rn   s
             r,   r;   zOlmo2RotaryEmbedding.forward  s2    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk^^UC 	&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C8	 	 	s    BE22E;r   )r@   rA   rB   r$   r   __annotations__r   r!   no_gradr   r;   rC   rD   s   @r,   r   r     s=    ll/{ /" U]]_
  
r-   r   c                   J    e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedZy)Olmo2PreTrainedModelr~   modelTr   r   )r8   
attentionsN)r@   rA   rB   r   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r}   _can_record_outputsr   r-   r,   r   r   (  sQ    &*#,-#4"5N!"&*$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 )
Olmo2Modelr~   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      r,   r!   zOlmo2Model.__init__=  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcivy1c
 !!3!39L9LM	.f=&+# 	 ds   D	input_idsrS   ro   r   inputs_embedsr   r   rV   rF   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_embedsr   r   r   )r   )r~   input_embedsrS   r   r   ro   )rS   ro   r   r   r   )last_hidden_stater   )
ValueErrorr   r
   r~   get_seq_lengthr$   aranger>   r   ri   r   r  r   r   r  r   )r(   r  rS   ro   r   r  r   r   rV   past_seen_tokensre   r8   r   decoder_layers                 r,   r;   zOlmo2Model.forwardM  s[    -t";<YZZ *.*;*;I*FM0*$++>O!CRC^==?de+0<< "2]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oom\J![[)H4;;+H+HI 		M)*) /-$7 M		 		-0&++
 	
r-   )NNNNNNN)r@   rA   rB   r   r!   r   r   r   r$   r   r   r	   FloatTensorr   r   r   r   r;   rC   rD   s   @r,   r   r   ;  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 )Olmo2ForCausalLMzlm_head.weightlm_headcolwise_repr8   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y r   )
r    r!   r   r   r   r"   r   r)   r  r  r   s     r,   r!   zOlmo2ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r-   r  rS   ro   r   r  labelsr   r   logits_to_keeprV   rF   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, Olmo2ForCausalLM

        >>> model = Olmo2ForCausalLM.from_pretrained("meta-olmo2/Olmo2-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo2/Olmo2-2-7b-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  rS   ro   r   r  r   r   N)r  r  r   )lossr  r   r8   r   r   )r   r	  r   r   slicer  loss_functionr~   r   r   r   r8   r   )r(   r  rS   ro   r   r  r  r   r   r  rV   outputsr8   slice_indicesr  r  s                   r,   r;   zOlmo2ForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r-   )	NNNNNNNNr   )r@   rA   rB   _tied_weights_keys_tp_plan_pp_planr!   r   r   r   r$   r   r   r	   r  r   r   r   r   r   r   r;   rC   rD   s   @r,   r  r    s0   *+=)H_-z:;H  151537+/59-1$(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  )r  r   r   )r   )Nr   );typingr   r   r   r$   torch.nnr"   transformers.utils.genericr   activationsr   cache_utilsr	   r
   
generationr   integrationsr   masking_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.deprecationr   utils.genericr   configuration_olmo2r   Moduler   r   r   rN   r   rg   ru   rj   r}   r   r   r   r   r   r  __all__r   r-   r,   <module>r4     s   - ,   9 ! . ) 7 / 9 O K F & 5 0 / , Y'J299 J (J(	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%428(J)RYY J)Zryy  )2 )X 299  F ?  $ K
% K
 K
\ H
+_ H
 H
V Er-   