
    ^j6                        d 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
 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mZmZmZ ddlmZmZ ddlmZmZmZm Z m!Z!m"Z" ddl#m$Z$  ejJ                  e&      Z' G d dejP                        Z) G d de!      Z*d Z+d$dZ, G d de      Z-e G d de             Z. G d de
      Z/ G d d e       Z0 G d! d"e      Z1g d#Z2y)%zPyTorch Cohere model.    )CallableN)nn   )Cache)FlashAttentionKwargs)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)dynamic_rope_update)ALL_ATTENTION_FUNCTIONS)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)maybe_autocastno_inherit_decorator   )LlamaAttentionLlamaForCausalLMLlamaMLP
LlamaModelLlamaRotaryEmbeddingeager_attention_forward   )CohereConfigc                   &     e Zd Zd fd	Zd Z xZS )CohereLayerNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)zcThe hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dimN)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeepsbias	__class__s       t/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/cohere/modular_cohere.pyr!   zCohereLayerNorm.__init__5   s/    ll5::k#:; #    c                    |j                   }|j                  t        j                        }|j	                  dd      }||z
  j                  d      j	                  dd      }||z
  t        j                  || j                  z         z  }| j                  j                  t        j                        |z  }|j                  |      S )NT)keepdimr   )	dtypetor#   float32meanpowrsqrtr&   r%   )r'   hidden_statesinput_dtyper4   variances        r,   forwardzCohereLayerNorm.forward;   s    #))%((7!!"d!3!D(--a055b$5G&-XH]H]=]1^^u}}5E,,r-   )Ngh㈵>F)__name__
__module____qualname__r!   r:   __classcell__r+   s   @r,   r   r   4   s    $-r-   r   c                   D    e Zd Z ej                         ed               Zy)CohereRotaryEmbeddingc                    | j                   d d d d f   j                         j                  |j                  d   dd      }|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                  |d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/   r   mpscpuF)device_typeenabledr   dimr1   )inv_freqfloatexpandshape
isinstancedevicetypestrr   	transposer#   repeat_interleavecosattention_scalingsinr2   r1   )
r'   xposition_idsinv_freq_expandedposition_ids_expandedrE   freqsembrT   rV   s
             r,   r:   zCohereRotaryEmbedding.forwardF   s@    !MM$4-8>>@GGHZHZ[\H]_acde ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfkUC 	5&,,.1F1L1L1NNYYZ[]^_E))%;C'')d444C'')d444C		5 vvAGGv$cff177f&;;;	5 	5s   =BFF
N)r;   r<   r=   r#   no_gradr   r:    r-   r,   rA   rA   E   s$    U]]_<  <r-   rA   c                     | dd d df   }| ddd df   }t        j                  | |gd      j                  d      }|S )N.r   r   r/   rG   )r#   stackflatten)rW   x1x2rot_xs       r,   rotate_halfrf   V   sL    	
3!8B	
319BKK"b	r*2226ELr-   c                 6   | j                   }| 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.
    rI   )r1   rK   	unsqueezerf   r2   )qkrT   rV   unsqueeze_dimr1   q_embedk_embeds           r,   apply_rotary_pos_embrn   ^   s    $ GGE		A		A
--
&C
--
&C3w;q>C/0G3w;q>C/0G::E:"GJJUJ$;;;r-   c                        e Zd Z fdZ xZS )	CohereMLPc                 J   t         |   |       t        j                  | j                  | j
                  d      | _        t        j                  | j                  | j
                  d      | _        t        j                  | j
                  | j                  d      | _        y )NF)r*   )	r    r!   r   Linearr(   intermediate_size	gate_projup_proj	down_projr'   configr+   s     r,   r!   zCohereMLP.__init__{   ss     4#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXr-   )r;   r<   r=   r!   r>   r?   s   @r,   rp   rp   z   s    Y Yr-   rp   c                        e Zd ZdZddededz  f fdZ	 ddej                  de	ej                  ej                  f   dej                  dz  d	e
dz  d
ee   de	ej                  ej                  dz  f   fdZ xZS )CohereAttentionz=Multi-headed attention from 'Attention Is All You Need' paperNrx   	layer_idxc                 *   t         |   ||       |j                  | _        | j                  ret        |j                  | j
                  f|j                        | _        t        |j                  | j
                  f|j                        | _	        y y Nr(   r)   )
r    r!   use_qk_normr   num_attention_headshead_dimlayer_norm_epsq_normnum_key_value_headsk_normr'   rx   r{   r+   s      r,   r!   zCohereAttention.__init__   s|    +!--)#77GVMbMbDK *#77GVMbMbDK r-   r7   position_embeddingsattention_maskpast_key_valueskwargsreturnc                 r   |j                   d d }g |d| j                  }| j                  |      j                  |      }| j	                  |      j                  |      }	| j                  |      j                  |      }
| j                  r"| j                  |      }| j                  |	      }	|j                  dd      }|	j                  dd      }	|
j                  dd      }
|\  }}t        ||	||      \  }}	| |j                  |	|
| j                        \  }	}
t        j                  | j                  j                   t"              } || ||	|
|f| j$                  sdn| j&                  | j(                  d|\  }} |j*                  g |d j-                         }| j/                  |      }||fS )Nr/   r   r   g        )dropoutscaling)rM   r   q_projviewk_projv_projr   r   r   rR   rn   updater{   r   get_interfacerx   _attn_implementationr   trainingattention_dropoutr   reshape
contiguouso_proj)r'   r7   r   r   r   r   input_shapehidden_shapequery_states
key_statesvalue_statesrT   rV   attention_interfaceattn_outputattn_weightss                   r,   r:   zCohereAttention.forward   s    $))#2.88b8$--8{{=166|D[[/44\B
{{=166|D;;|4LZ0J#--a3))!Q/
#--a3&S#7jRUWZ#[ j&'6'='=j,X\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r-   N)r;   r<   r=   __doc__r   intr!   r#   Tensortupler   r   r   r:   r>   r?   s   @r,   rz   rz      s    G
| 
d
 
" )-.)||.) #5<<#=>.) t+	.)
 .) -..) 
u||U\\D00	1.)r-   rz   c                   D    e Zd Zdedef fdZ	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   deej                  eej                  ej                  f   dz  f   fdZ xZS )CohereDecoderLayerrx   r{   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        y )N)rx   r{   r~   )
r    r!   r(   rz   	self_attnrp   mlpr   r   input_layernormr   s      r,   r!   zCohereDecoderLayer.__init__   sR    !--()LV$.F<N<NU[UjUjkr-   Nr7   r   rX   r   	use_cacher   r   r   c           
          |}| j                  |      } | j                  d||||||d|\  }	}
| j                  |      }||	z   |z   }|S )a  
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            past_key_values (`Cache`, *optional*): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
        )r7   r   rX   r   r   r   r^   )r   r   r   )r'   r7   r   rX   r   r   r   r   residualhidden_states_attention_hidden_states_mlps               r,   r:   zCohereDecoderLayer.forward   sx    6 !,,];%3T^^ &
')%+ 3&
 &
" !HH]3 #::=NNr-   )NNNFN)r;   r<   r=   r   r   r!   r#   r   
LongTensorr   boolr   r   r   FloatTensorr:   r>   r?   s   @r,   r   r      s    l| l l /304(,!&HL*||* t+* &&-	*
 * $;* #5<<#=>E* -.* 
u  %(9(95;L;L(L"MPT"TT	U*r-   r   c                   $     e Zd Zdef fdZ xZS )CohereModelrx   c           	         t         |   |       t        j                  t	        |j
                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        y c c}w r}   )r    r!   r   
ModuleListrangenum_hidden_layersr   layersr   r(   r   normr   s      r,   r!   zCohereModel.__init__   sc     mmDI&JbJbDcdy	2d
 $1C1C&J_J_`	 es   A=)r;   r<   r=   r   r!   r>   r?   s   @r,   r   r      s    a| a ar-   r   c                   "    e Zd Z fdZee	 	 	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  de	dz  dej                  dz  dej                  dz  d	edz  d
eej                  z  dee   defd              Z xZS )CohereForCausalLMc                     t         |   |       t        |      | _        |j                  | _        |j
                  | _        y r   )r    r!   r   modellogit_scaletie_word_embeddingsrw   s     r,   r!   zCohereForCausalLM.__init__  s8      (
!--#)#=#= r-   N	input_idsr   rX   r   inputs_embedslabelsr   logits_to_keepr   r   c	           
          | j                   d||||||d|	}
|
j                  }t        |t              rt	        | d      n|}| j                  |dd|ddf         }|| j                  z  }d}|* | j                  d||| j                  j                  d|	}t        |||
j                  |
j                  |
j                        S )a  
        Example:

        ```python
        >> from transformers import AutoTokenizer, CohereForCausalLM

        >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
        >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")

        >> 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   rX   r   r   r   N)logitsr   
vocab_size)lossr   r   r7   
attentionsr^   )r   last_hidden_staterN   r   slicelm_headr   loss_functionrx   r   r
   r   r7   r   )r'   r   r   rX   r   r   r   r   r   r   outputsr7   slice_indicesr   r   s                  r,   r:   zCohereForCausalLM.forward  s    > ,64:: ,
)%+',
 ,
  118B>SV8W~ot4]kmA}a,?@A$***%4%%pVFt{{OeOepiopD%#33!//))
 	
r-   )NNNNNNNr   )r;   r<   r=   r!   r   r   r#   r   r   r   r   r   r   r   r   r
   r:   r>   r?   s   @r,   r   r     s    >  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r-   r   )r   r   CoherePreTrainedModel)r   )3r   collections.abcr   r#   r   cache_utilsr   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr	   r
   modeling_rope_utilsr   modeling_utilsr   processing_utilsr   utilsr   r   r   r   utils.genericr   r   llama.modeling_llamar   r   r   r   r   r   configuration_coherer   
get_loggerr;   loggerModuler   rA   rf   rn   rp   rz   r   r   r   __all__r^   r-   r,   <module>r      s   ,  $     B 9 O 6 5 & R R A  / 
		H	%-bii -"<0 <"<8Y Y =)n =) =)@23 2ja* a?
( ?
Dr-   