
    %	&h56                     h   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 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mZmZ  ej4                  e      Z G d de      Z G d de      Z ej>                  e        G d de      Z  G d de      Z! G d de      Z" G d de      Z#g dZ$y)    )CallableOptionalTupleN   )Cache)ALL_ATTENTION_FUNCTIONS)ALL_LAYERNORM_LAYERS)logging   )LlamaRMSNormeager_attention_forward)
OlmoConfig)OlmoAttentionOlmoDecoderLayerOlmoForCausalLM	OlmoModelapply_rotary_pos_embc                        e Zd ZdZdZddddddddZdgd	gfd
dgd
gfd
gd
gfdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )Olmo2Configa  
    This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf).

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 50304):
            Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Olmo2Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 1):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 50279):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.

    ```python
    >>> from transformers import Olmo2Model, Olmo2Config

    >>> # Initializing a Olmo2 7B style configuration
    >>> configuration = Olmo2Config()

    >>> # Initializing a model from the Olmo2 7B style configuration
    >>> model = Olmo2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    olmo2colwise_reprowwise_repcolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj	input_idsinputs_embedshidden_statesattention_mask)embed_tokenslayersnormc                     t        |   di d|d|d|d|d|d|d|d|d	|	d
|
d|d|d|d|d|d|d|d|| || _        | `y )N
vocab_sizehidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads
hidden_actmax_position_embeddingsinitializer_range	use_cachepad_token_idbos_token_ideos_token_idtie_word_embeddings
rope_thetarope_scalingattention_biasattention_dropout )super__init__rms_norm_epsclip_qkv)selfr#   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r8   kwargs	__class__s                        }/var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/olmo2/modular_olmo2.pyr7   zOlmo2Config.__init__v   s    . 	 	
!	
#	
 0	
 0		

 !4	
 !4	
 "	
 %<	
 0	
  	
 &	
 &	
 &	
 !4	
 "	
  &!	
" *#	
$ 0'	
, )M    )i  i   i +      r?   Nsilui   g{Gz?T   Nig  Fg     @NF        gh㈵>)	__name__
__module____qualname____doc__
model_typebase_model_tp_planbase_model_pp_planr7   __classcell__r<   s   @r=   r   r      s    KZ J%2%2%2%2"+ )"+ &(9:#%568IJ!"_$56   $!). .r>   r   c                       e Zd Zy)Olmo2RMSNormNrC   rD   rE   r5   r>   r=   rM   rM          r>   rM   c                   0    e Zd Zddedee   f fdZ	 	 ddej                  de	ej                  ej                  f   deej                     dee
   deej                     d	e	ej                  eej                     ee	ej                        f   fd
Z xZS )Olmo2Attentionconfig	layer_idxc                     t         |   ||       t        |j                  | j                  z  |j
                        | _        t        |j                  | j                  z  |j
                        | _        y )NrS   )	r6   r7   rM   r'   head_dimr8   q_normr(   k_normr:   rR   rS   r<   s      r=   r7   zOlmo2Attention.__init__   s[    95"6#=#=#MvObObc"6#=#=#MvObObcr>   r   position_embeddingsr   past_key_valuecache_positionreturnc                    |j                   d d }g |d| j                  }| j                  | j                  |            }	| j	                  | j                  |            }
| j                  |      }|	j                  |      j                  dd      }	|
j                  |      j                  dd      }
|j                  |      j                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  r^| j                  j                  dk(  r(|j                  dd      rt         j#                  d	       nt$        | j                  j                     } || |	|
||f| j&                  sd
n| j(                  | j*                  d|\  }} |j,                  g |d j/                         }| j1                  |      }||fS )NrA   r   )sincosr\   eagersdpaoutput_attentionsFz`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.rB   )dropoutscaling)shaperV   rW   q_projrX   k_projv_projview	transposer   updaterS   r   rR   _attn_implementationgetloggerwarning_oncer   trainingr4   rf   reshape
contiguouso_proj)r:   r   rZ   r   r[   r\   r;   input_shapehidden_shapequery_states
key_statesvalue_statesra   r`   cache_kwargsattention_interfaceattn_outputattn_weightss                     r=   forwardz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'5'<'<ZW[WeWegs't$J(?;;++w6{{//69fjjI\^c>d##L
 '>dkk>^>^&_#$7	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r>   )N)NN)rC   rD   rE   r   r   intr7   torchTensorr   r   
LongTensorr   rJ   rK   s   @r=   rQ   rQ      s    d{ dx} d +/593)||3) #5<<#=>3) !.	3)
 !3) !!1!123) 
u||Xell3XeELL>Q5RR	S3)r>   rQ   c                   f    e Zd Zdedef fdZ	 	 	 	 	 	 	 ddej                  deej                     deej                     dee
   dee   d	ee   d
eej                     deeej                  ej                  f      deej                  eeej                  ej                  f      f   fdZ xZS )Olmo2DecoderLayerrR   rS   c                     t         |   ||       t        |j                  |j                        | _        t        |j                  |j                        | _        t        ||      | _        | `	y )NrU   eps)rR   rS   )
r6   r7   rM   r$   r8   post_attention_layernormpost_feedforward_layernormrQ   	self_attninput_layernormrY   s      r=   r7   zOlmo2DecoderLayer.__init__   s_    95(4V5G5GVM`M`(a%*6v7I7IvObOb*c''vK r>   r   r   position_idsr[   rd   r,   r\   rZ   r]   c	                     |}
 | j                   d||||||||d|	\  }}| j                  |      }|
|z   }|}
| j                  |      }| j                  |      }|
|z   }|f}|r||fz  }|S )N)r   r   r   r[   rd   r,   r\   rZ   r5   )r   r   mlpr   )r:   r   r   r   r[   rd   r,   r\   rZ   r;   residualself_attn_weightsoutputss                r=   r   zOlmo2DecoderLayer.forward   s     ! ,:4>> 
,
')%)/) 3
,
 
,
(( 55mD =0 !/77F =0 ")++Gr>   )NNNFFNN)rC   rD   rE   r   r   r7   r   r   r   r   r   boolr   FloatTensorr   rJ   rK   s   @r=   r   r      s    !{ !s ! 2637*.,1$)59KO'||' !.' u//0	'
 !' $D>' D>' !!1!12' &eELL%,,,F&GH' 
u  (51B1BEDUDU1U+V"WW	X'r>   r   c                   $     e Zd Zdef fdZ xZS )
Olmo2ModelrR   c           	         t         |   |       t        |j                  |j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        y c c}w )Nr   )r6   r7   rM   r$   r8   r!   nn
ModuleListranger&   r   r    rY   s      r=   r7   zOlmo2Model.__init__%  s^      !3!39L9LM	mmCHIaIaCbcivy1c
cs   A=)rC   rD   rE   r   r7   rJ   rK   s   @r=   r   r   $  s    
{ 
 
r>   r   c                       e Zd Zy)Olmo2ForCausalLMNrN   r5   r>   r=   r   r   .  rO   r>   r   )r   r   r   Olmo2PreTrainedModel)%typingr   r   r   r   torch.nnr   cache_utilsr   modeling_utilsr   pytorch_utilsr	   utilsr
   llama.modeling_llamar   r   olmo.configuration_olmor   olmo.modeling_olmor   r   r   r   r   
get_loggerrC   rp   r   rM   appendrQ   r   r   r   __all__r5   r>   r=   <module>r      s    , ,     5 1  H 0  
		H	%L* L^	< 	    L )9)] 9)~/( /h
 
	 	r>   