
    ivX                     2   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
 ddlmZ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 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'm(Z( ddl)m*Z*  ed       G d dejV                               Z,dejZ                  de.dejZ                  fdZ/	 d5dejV                  dejZ                  dejZ                  dejZ                  dejZ                  dz  d e0d!e0d"e"e   fd#Z1d6d$Z2d% Z3 ee2       G d& d'ejV                               Z4 G d( d)ejV                        Z5 G d* d+e      Z6 G d, d-ejV                        Z7e$ G d. d/e              Z8e$ G d0 d1e8             Z9e$ G d2 d3e8e             Z:g d4Z;y)7    )Callable)OptionalN)TransformersKwargs   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernelized_func)create_causal_mask!create_sliding_window_causal_mask)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)auto_docstringcan_return_tuple)check_model_inputsmaybe_autocast   )Olmo3ConfigRMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )Olmo3RMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z;
        Olmo3RMSNorm is equivalent to T5LayerNorm
        N)super__init__nn	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      r/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/olmo3/modeling_olmo3.pyr"   zOlmo3RMSNorm.__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Olmo3RMSNorm.forward6   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Olmo3RMSNorm.extra_repr=   s*    ))*+6$2G2G1HIIr.   )gư>)__name__
__module____qualname__r"   r<   r@   __classcell__r,   s   @r-   r   r   ,   s    $=Jr.   r   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?   expandreshape)r9   rF   batchnum_key_value_headsslenhead_dims         r-   	repeat_kvrO   A   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   )dimr3   )ptrainingr   )rO   num_key_value_groupsr%   matmul	transposer?   r#   
functionalsoftmaxr5   r4   r3   rV   r\   
contiguous)rP   rQ   rR   rS   rT   rU   rV   rW   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r-   eager_attention_forwardrh   M   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.
        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.
    )r3   	unsqueezerotate_halfr4   )	qkcossinunsqueeze_dimq_typek_typeq_embedk_embeds	            r-   apply_rotary_pos_embru   g   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..Nr1   r0   rZ   )r?   r%   cat)xx1x2s      r-   rk   rk      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r.   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j                  dz  dee   de	ej                  ej                  dz  f   fdZ xZS )Olmo3Attentionz=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*                        | _        |j0                  J |j0                  |   | _        | j2                  dk(  r|j4                  | _        y d | _        y )NrN   g      Tbiassliding_attention)r!   r"   r~   r   getattrr*   num_attention_headsrN   rL   r]   rU   attention_dropout	is_causalr#   Linearattention_biasq_projk_projv_projo_projr   rms_norm_epsq_normk_normlayer_typesattention_typesliding_windowr)   r~   r   r,   s      r-   r"   zOlmo3Attention.__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!!---$00;7;7J7JNa7af33gkr.   Nr9   position_embeddingsrT   past_key_valuescache_positionrW   rG   c                 v   |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                  j                  t               } || |	|
||f| j"                  sdn| j$                  | j&                  | j(                  d|\  }} |j*                  g |d j-                         }| j/                  |      }||fS )Nr1   r   r0   )ro   rn   r           )rV   rU   r   )r?   rN   r   r   r   r   r   viewr_   ru   updater   r   get_interfacer~   _attn_implementationrh   r\   r   rU   r   rJ   rb   r   )r)   r9   r   rT   r   r   rW   input_shapehidden_shapequery_statesrc   rd   rn   ro   cache_kwargsattention_interfacerg   re   s                     r-   r<   zOlmo3Attention.forward   s    $))#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(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
!\ *k));;;;FFHkk+.L((r.   )NN)rA   rB   rC   __doc__r   intr"   r%   Tensorr>   r   
LongTensorr   r   r<   rD   rE   s   @r-   r}   r}      s    Gl{ ls lB )-26.)||.) #5<<#=>.) t+	.)
 .) ((4/.) +,.) 
u||U\\D00	1.)r.   r}   c                   $     e Zd Z fdZd Z xZS )Olmo3MLPc                    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Olmo3MLP.__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 N)r   r   r   r   )r)   ry   r   s      r-   r<   zOlmo3MLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r.   )rA   rB   rC   r"   r<   rD   rE   s   @r-   r   r      s    0r.   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j                  dz  deej                  ej                  f   dz  dee   dej                  fdZ xZS )Olmo3DecoderLayerr~   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Olmo3DecoderLayer.__init__   sl    !--'vKF#(4V5G5GVM`M`(a%*6v7I7IvObOb*c'r.   Nr9   rT   position_idsr   	use_cacher   r   rW   rG   c                     |}	 | j                   d|||||||d|\  }}
| j                  |      }|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)r9   rT   r   r   r   r   r    )r   r   r   r   )r)   r9   rT   r   r   r   r   r   rW   residual_s              r-   r<   zOlmo3DecoderLayer.forward   s     !)4>> 	
')%+) 3	
 	
q 55mD =0 !/77F =0r.   )NNNFNN)rA   rB   rC   r   r   r"   r%   r   r   r   boolr>   r   r   r<   rD   rE   s   @r-   r   r      s    d{ ds d /304(,!&26HL|| t+ &&-	
  $; ((4/ #5<<#=>E +, 
r.   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 )Olmo3RotaryEmbedding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defaultr   F)
persistentoriginal_inv_freq)r!   r"   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr~   rope_parametersr   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r)   r~   devicerope_init_fnr   r,   s        r-   r"   zOlmo3RotaryEmbedding.__init__  s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuUr.   r   ztorch.deviceseq_lenrG   z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_thetarN   Ng      ?r   r0   r3   )r   r3   )	r   r   r*   r   r%   arangeint64r4   float)r~   r   r   baserZ   attention_factorr   s          r-   r   z4Olmo3RotaryEmbedding.compute_default_rope_parameters'  s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r.   c                 N   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r1   r   mpscpuF)device_typeenabledr0   rw   r   )r   r   rI   r?   r4   r   
isinstancetypestrr   r_   r%   rx   rn   r   ro   r3   )
r)   ry   r   inv_freq_expandedposition_ids_expandedr   freqsembrn   ro   s
             r-   r<   zOlmo3RotaryEmbedding.forwardE  sR    !MM$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)rA   rB   rC   r%   r   __annotations__r   r"   staticmethodr   r   r>   r   r   no_gradr   r<   rD   rE   s   @r-   r   r     s    llV{ V  %)+/"*d"*(* t* 
~u$	%	* *: 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)Olmo3PreTrainedModelr~   modelTr   r   )r9   
attentionsN)rA   rB   rC   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   U  sQ    &*#,-#4"5N!"&*$r.   r   c                       e Zd Zdef fdZee	 	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  de
dz  dej                  dz  d	ej                  dz  d
edz  dee   defd              Z xZS )
Olmo3Modelr~   c           	         t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   r~   F)r!   r"   pad_token_idpadding_idx
vocab_sizer#   	Embeddingr*   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointing	post_initr   s      r-   r"   zOlmo3Model.__init__j  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcivy1c
 !!3!39L9LM	.f=&+# 	 ds   DN	input_idsrT   r   r   inputs_embedsr   r   rW   rG   c           
         |d u |d uz  rt        d      || j                  |      }|r|t        | j                        }|E||j	                         nd}	t        j                  |j                  d   |j                        |	z   }||j                  d      }t        |x}
t              s*| j                  |||||d}t        d
i |t        d
i |d}
|}| j                  ||      }| j                  d | j                  j                    D ](  } ||f|
|j"                  j$                     ||||d|}* | j'                  |      }t)        ||	      S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r   )r   )r~   input_embedsrT   r   r   r   )full_attentionr   )rT   r   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r~   get_seq_lengthr%   r   r?   r   rj   r   dictr   r   r	  r  r  r   r   r  r   )r)   r  rT   r   r   r  r   r   rW   past_seen_tokenscausal_mask_mappingmask_kwargsr9   r   decoder_layers                  r-   r<   zOlmo3Model.forwardz  s    -t";<YZZ *.*;*;I*FM0*$++>O!CRC^==?de]003M<P<PQTdd  )33A6L ?-F ++ -"0"0#2 ,K #5"C{"C%F%U%U#
 &"oom\J![[)H4;;+H+HI 		M)2=3J3J3Y3YZ) /-$7 M		 		-0&++
 	
r.   )NNNNNNN)rA   rB   rC   r   r"   r   r   r%   r   r   r   FloatTensorr   r   r   r   r<   rD   rE   s   @r-   r   r   h  s    {    .2.204(,2626!%@
##d*@
 t+@
 &&-	@

 @
 ((4/@
 ((4/@
 $;@
 +,@
 
!@
  @
r.   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	j                  dz  dee	j                  z  dee   defd              Z xZS )Olmo3ForCausalLMzlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr9   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Olmo3ForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r.   Nr  rT   r   r   r  labelsr   r   logits_to_keeprW   rG   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, Olmo3ForCausalLM

        >>> model = Olmo3ForCausalLM.from_pretrained("meta-olmo3/Olmo3-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo3/Olmo3-2-7b-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  rT   r   r   r  r   r   N)r  r   r  )lossr  r   r9   r   r   )r   r  r   r   slicer  loss_functionr~   r  r   r   r9   r   )r)   r  rT   r   r   r  r   r   r   r!  rW   outputsr9   slice_indicesr  r#  s                   r-   r<   zOlmo3ForCausalLM.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   r   r  r   r   r   r   r   r<   rD   rE   s   @r-   r  r    s/   *,GH23H_-z:;H  .2.204(,26*.!%26-.8
##d*8
 t+8
 &&-	8

 8
 ((4/8
   4'8
 $;8
 ((4/8
 ell*8
 +,8
 
 8
  8
r.   r  )r  r   r   )r   )r   )<collections.abcr   typingr   r%   torch.nnr#   transformers.utils.genericr   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   masking_utilsr   r   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.genericr   r   configuration_olmo3r   Moduler   r   r   rO   r   rh   ru   rk   r}   r   r   r   r   r   r  __all__r   r.   r-   <module>r>     s  * %    9 ! . ) L R 9 O K F & 5 ? , Y'J299 J (J(	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%424( )*M)RYY M) +M)`ryy  (2 (V><299 ><B ?  $ S
% S
 S
l H
+_ H
 H
V Er.   