
    iV                     f   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 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+  G d dejX                        Z-d Z. ed      d9d       Z/dej`                  de1dej`                  fdZ2	 d:dejX                  dej`                  d ej`                  d!ej`                  d"ej`                  dz  d#e3d$e3d%e"e$   fd&Z4 ee/       G d' d(ejX                               Z5 ed)       G d* d+ejX                               Z6 G d, d-ejX                        Z7 G d. d/e      Z8e% G d0 d1e              Z9 G d2 d3e      Z:e% G d4 d5e9             Z;e% G d6 d7e9e             Z<g d8Z=y);    )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)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)check_model_inputsmaybe_autocast   )	CwmConfigc                        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 )CwmRotaryEmbeddinginv_freqNconfigc                    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)super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr#   rope_parametersr%   compute_default_rope_parametersr   attention_scalingregister_bufferclone)selfr#   devicerope_init_fnr"   	__class__s        n/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/cwm/modeling_cwm.pyr*   zCwmRotaryEmbedding.__init__/   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuU    r4   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)r4   r@   )	r.   getattrhidden_sizenum_attention_headstorcharangeint64tofloat)r#   r4   r9   basedimattention_factorr"   s          r7   r/   z2CwmRotaryEmbedding.compute_default_rope_parameters?   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r8   c                 N   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r   mpscpuF)device_typeenabledr>   rJ   r?   )r"   rH   expandshaperG   r4   
isinstancetypestrr   	transposerD   catcosr0   sinr@   )
r3   xposition_idsinv_freq_expandedposition_ids_expandedrP   freqsembrZ   r[   s
             r7   forwardzCwmRotaryEmbedding.forward]   sR    !MM$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$cff177f&;;;	5 	5s   BFF$N)NNN)__name__
__module____qualname__rD   Tensor__annotations__r   r*   staticmethodr   inttuplerH   r/   no_gradr   rb   __classcell__r6   s   @r7   r!   r!   ,   s    llVy V  #'+/"*D *(* t* 
~u$	%	* *: U]]_<  <r8   r!   c                     | dd| j                   d   dz  f   }| d| j                   d   dz  df   }t        j                  | |fd      S )z*Rotates half the hidden dims of the input..NrM   r>   rR   )rT   rD   rY   )r\   x1x2s      r7   rotate_halfrr   m   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r8   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.
    )	unsqueezerr   )qkrZ   r[   unsqueeze_dimq_embedk_embeds          r7   apply_rotary_pos_embr{   t   sY    & --
&C
--
&C3w;q>C/0G3w;q>C/0GGr8   hidden_statesn_repr:   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)rT   rS   reshape)r|   r}   batchnum_key_value_headsslenr=   s         r7   	repeat_kvr      so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr8   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   rM   )rJ   r@   )ptrainingr   )r   num_key_value_groupsrD   matmulrX   rT   r   
functionalsoftmaxfloat32rG   r@   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r7   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$$r8   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 )CwmAttentionz=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                  | j                  z  d      | _        t         j"                  j%                  |j                  |j                  | j                  z  d      | _        t         j"                  j%                  |j                  |j                  | j                  z  d      | _        t#        j$                  |j                  | j                  z  |j                  d      | _        | j                  dk(  r|j.                  | _        y d | _        y )Nlayer_typesr=   g      TFbiassliding_attention)r)   r*   hasattrr   
layer_typer#   r   rA   rB   rC   r=   r   r   r   attention_dropout	is_causalrD   r   Linearq_projk_projv_projo_projsliding_windowr3   r#   r   r6   s      r7   r*   zCwmAttention.__init__   s~   ;B6=;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9hhoof&8&8&:T:TW[WdWd:dkpoqhhoof&8&8&:T:TW[WdWd:dkpoqhhoof&8&8&:T:TW[WdWd:dkpoqii : :T]] JFL^L^ejk7;J]7]f33cgr8   Nr|   position_embeddingsr   past_key_valuescache_positionr   r:   c                 .   |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      j                  |      j	                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        j                  | j                  j                  t              } || |	|
||f| j                  sdn| j                   | j"                  | j$                  d|\  }} |j&                  g |d j)                         }| j+                  |      }||fS )NrM   r   r>   )r[   rZ   r           )r   r   r   )rT   r=   r   viewrX   r   r   r{   updater   r   get_interfacer#   _attn_implementationr   r   r   r   r   r   r   r   )r3   r|   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rZ   r[   cache_kwargsattention_interfacer   r   s                     r7   rb   zCwmAttention.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(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
!\ *k));;;;FFHkk+.L((r8   )NN)rd   re   rf   __doc__r   rj   r*   rD   rg   rk   r   
LongTensorr   r   rb   rm   rn   s   @r7   r   r      s    Ghy hS h* )-26*)||*) #5<<#=>*) t+	*)
 *) ((4/*) -.*) 
u||U\\D00	1*)r8   r   RMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )
CwmRMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z9
        CwmRMSNorm is equivalent to T5LayerNorm
        N)r)   r*   r   	ParameterrD   onesweightvariance_epsilon)r3   rB   epsr6   s      r7   r*   zCwmRMSNorm.__init__   s1     	ll5::k#:; #r8   c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )Nr>   rM   T)keepdim)	r@   rG   rD   r   powmeanrsqrtr   r   )r3   r|   input_dtypevariances       r7   rb   zCwmRMSNorm.forward   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r8   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)rk   r   rT   r   )r3   s    r7   
extra_reprzCwmRMSNorm.extra_repr  s*    ))*+6$2G2G1HIIr8   )gư>)rd   re   rf   r*   rb   r   rm   rn   s   @r7   r   r      s    $;Jr8   r   c                   $     e Zd Z fdZd Z xZS )CwmMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  |j                        | _        t        j                  | j                  | j                  |j                        | _	        t        j                  | j                  | j                  |j                        | _
        t        |j                     | _        y )Nr   )r)   r*   r#   rB   intermediate_sizer   r   mlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr3   r#   r6   s     r7   r*   zCwmMLP.__init__  s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r8   c                     | j                  | j                  | j                  |            | j                  |      z        }|S rc   )r   r   r   r   )r3   r\   r   s      r7   rb   zCwmMLP.forward  s6    NN4;;t~~a/@#ADLLQRO#ST	r8   )rd   re   rf   r*   rb   rm   rn   s   @r7   r   r   
  s    0r8   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 )CwmDecoderLayerr#   r   c                 H   t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        |j                  |   | _        y )N)r#   r   r   )r)   r*   rB   r   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   attention_typer   s      r7   r*   zCwmDecoderLayer.__init__  s~    !--%VyI&>)&*<*<&BUBUV(263E3E6K^K^(_%$00;r8   Nr|   r   r]   r   	use_cacher   r   r   r:   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)r|   r   r]   r   r   r   r    )r   r   r   r   )r3   r|   r   r]   r   r   r   r   r   residual_s              r7   rb   zCwmDecoderLayer.forward%  s     !,,];)4>> 	
')%+) 3	
 	
q !=0 !55mD/ =0r8   )NNNFNN)rd   re   rf   r   rj   r*   rD   rg   r   r   boolrk   r   r   rb   rm   rn   s   @r7   r   r     s    <y <S < /304(,!&26HL|| t+ &&-	
  $; ((4/ #5<<#=>E +, 
r8   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)CwmPreTrainedModelr#   modelTr   r   )r|   
attentionsN)rd   re   rf   r   rh   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   r8   r7   r   r   G  sQ    &*#*+#4"5N!"&("r8   r   c                       e Zd Zy)CwmModelOutputWithPastN)rd   re   rf   r   r8   r7   r   r   Z  s    r8   r   c                       e Zd Ze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 )CwmModelr#   c           	          t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j
                  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   	EmbeddingrB   embed_tokensrD   
ModuleListrangenum_hidden_layersr   layersr   r   normr!   
rotary_embgradient_checkpointing	post_initr   s      r7   r*   zCwmModel.__init__b  s     !.. ++LL):):F<N<NPTP`P`ahh))AFvG_G_A`aI_VY/a
 v11v7J7JK	,F;&+# 	 bs   DN	input_idsr   r]   r   inputs_embedsr   r   r   r:   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}|j                         }t        d
i |t        d
i |d}
|}| j                  ||      }| j                   d | j                  j"                   D ]  } ||f|
|j$                     ||||d|}  | j'                  |      }t)        ||	      S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r   )r4   )r#   input_embedsr   r   r   r]   )full_attentionr   )r   r]   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r#   get_seq_lengthrD   rE   rT   r4   ru   rU   dictcopyr   r   r  r	  r  r   r
  r   )r3   r  r   r]   r   r  r   r   r   past_seen_tokenscausal_mask_mappingmask_kwargssliding_mask_kwargsr|   r   decoder_layers                   r7   rb   zCwmModel.forwardr  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 #."2"2"4 #5"C{"C%F%]I\%]#
 &"oom\J![[)H4;;+H+HI 		M)2=3O3OP) /-$7 M		 		-0%++
 	
r8   )NNNNNNN)rd   re   rf   r   config_classr*   r   r   rD   r   rg   r   FloatTensorr   r   r   r   rb   rm   rn   s   @r7   r   r   ^  s    Ly    .2.204(,2626!%?
##d*?
 t+?
 &&-	?

 ?
 ((4/?
 ((4/?
 $;?
 +,?
 
 ?
  ?
r8   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 )CwmForCausalLMzlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr|   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFr   )
r)   r*   r   r   r  r   r   rB   r!  r  r   s     r7   r*   zCwmForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FUS 	r8   Nr  r   r]   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, CwmForCausalLM

        >>> model = CwmForCausalLM.from_pretrained("meta-cwm/Cwm-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-cwm/Cwm-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   r]   r   r  r   r   N)r#  r%  r  )lossr#  r   r|   r   r   )r   r  rU   rj   slicer!  loss_functionr#   r  r   r   r|   r   )r3   r  r   r]   r   r  r%  r   r   r&  r   outputsr|   slice_indicesr#  r(  s                   r7   rb   zCwmForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r8   )	NNNNNNNNr   )rd   re   rf   _tied_weights_keys_tp_plan_pp_planr*   r   r   rD   r   rg   r   r  r   rj   r   r   r   rb   rm   rn   s   @r7   r   r     s/   *,GH23H_-z:;H  .2.204(,26*.!%26-.8
##d*8
 t+8
 &&-	8

 8
 ((4/8
   4'8
 $;8
 ((4/8
 ell*8
 +,8
 
 8
  8
r8   r   )r   r   r   )r   )r   )>collections.abcr   typingr   rD   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   configuration_cwmr   Moduler!   rr   r{   rg   rj   r   rH   r   r   r   r   r   r   r   r   r   __all__r   r8   r7   <module>rB     s  , %    ! . ) f f R B 9 O K F & I I ? (>< ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%4 )*=)299 =) +=)@ Y'J J (J(RYY  *0 *Z   $	4 	 T
! T
 T
n H
' H
 H
V ?r8   