o
    Zh9                     @   s@  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mZ dd
lmZ ddlmZmZmZmZmZmZ eeZG dd deZG dd deZ e!e  dd Z"G dd deZ#G dd deZ$G dd deZ%G dd deZ&G dd deZ'G dd deZ(g dZ)dS )    )CallableOptionalTupleN   )Cache)ALL_ATTENTION_FUNCTIONS)ALL_LAYERNORM_LAYERS)logging   )LlamaPreTrainedModelLlamaRMSNormeager_attention_forward)
OlmoConfig)OlmoAttentionOlmoDecoderLayerOlmoForCausalLM	OlmoModelOlmoRotaryEmbeddingapply_rotary_pos_embc                       s   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d	Z  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
    ```
    Zolmo2Zcolwise_repZrowwise_repZcolwiseZ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_projZ	input_idsZinputs_embedshidden_statesattention_mask)Zembed_tokenslayersnorm      +      Nsilu   {Gz?T   g  F     @        h㈵>c                    s   t  jdi d|d|d|d|d|d|d|d|d	|	d
|
d|d|d|d|d|d|d|d|| || _| `d S )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_epsZclip_qkv)selfr&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   r6   r7   r;   kwargs	__class__r8   V/var/www/auris/lib/python3.10/site-packages/transformers/models/olmo2/modular_olmo2.pyr:   w   sP   	
zOlmo2Config.__init__)r   r   r   r   r   Nr   r   r    Tr!   Nr"   Fr#   NFr$   r%   )	__name__
__module____qualname____doc__Z
model_typeZbase_model_tp_planZbase_model_pp_planr:   __classcell__r8   r8   r>   r@   r      sF    M


r   c                   @   s   e Zd Zdd ZdS )Olmo2RMSNormc                 C   sJ   |j }|tj}|djddd}|t|| j  }| j| |S )Nr
   T)Zkeepdim)	ZdtypetotorchZfloat32powmeanZrsqrtZvariance_epsilonweight)r<   r   Zinput_dtypeZvariancer8   r8   r@   forward   s
   zOlmo2RMSNorm.forwardN)rA   rB   rC   rM   r8   r8   r8   r@   rF      s    rF   c                 C   sH   | dd| j d d f }| d| j d d df }tj| |fddS )z*Rotates half the hidden dims of the input..NrG   r
   )dim)shaperI   cat)xx1Zx2r8   r8   r@   rotate_half   s   rS   c                       s   e Zd Zddedee f fd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dZ  ZS )Olmo2AttentionNconfig	layer_idxc                    s@   t  j||d t|j| j |j| _t|j| j |j| _d S )NrV   )	r9   r:   rF   r*   head_dimr;   q_normr+   k_normr<   rU   rV   r>   r8   r@   r:      s   zOlmo2Attention.__init__r   position_embeddingsr   past_key_valuecache_positionreturnc                 K   s`  |j d d }g |d| jR }| | |}	| | |}
| |}|	|dd}	|
|dd}
||dd}|\}}t	|	|
||\}	}
|d urc|||d}|
|
|| j|\}
}t}| jjdkr| jjdkr}|ddr}td	 nt| jj }|| |	|
||f| jsd
n| j| jd|\}}|jg |dR   }| |}||fS )NrG   r!   r
   )sincosr^   eagerZ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.r$   )Zdropoutscaling)rO   rX   rY   Zq_projrZ   Zk_projZv_projviewZ	transposer   updaterV   r   rU   Z_attn_implementationgetloggerZwarning_oncer   Ztrainingr7   rd   Zreshape
contiguousZo_proj)r<   r   r\   r   r]   r^   r=   Zinput_shapeZhidden_shapeZquery_statesZ
key_statesZvalue_statesra   r`   Zcache_kwargsZattention_interfaceZattn_outputZattn_weightsr8   r8   r@   rM      sF   	


zOlmo2Attention.forward)N)NN)rA   rB   rC   r   r   intr:   rI   Tensorr   r   
LongTensorrM   rE   r8   r8   r>   r@   rT      s"    
rT   c                       s   e Zd Zdedef fd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dZ  ZS )Olmo2DecoderLayerrU   rV   c                    sJ   t  j||d t|j|jd| _t|j|jd| _t||d| _| `	d S )NrW   Zeps)rU   rV   )
r9   r:   rF   r'   r;   post_attention_layernormpost_feedforward_layernormrT   	self_attnZinput_layernormr[   r>   r8   r@   r:      s
   zOlmo2DecoderLayer.__init__NFr   r   position_idsr]   rc   r/   r^   r\   r_   c	                 K   st   |}
| j d||||||||d|	\}}| |}|
| }|}
| |}| |}|
| }|f}|r8||f7 }|S )N)r   r   rr   r]   rc   r/   r^   r\   r8   )rq   ro   Zmlprp   )r<   r   r   rr   r]   rc   r/   r^   r\   r=   ZresidualZself_attn_weightsZoutputsr8   r8   r@   rM     s.   	




zOlmo2DecoderLayer.forward)NNNFFNN)rA   rB   rC   r   rj   r:   rI   rk   r   rl   r   boolr   ZFloatTensorrM   rE   r8   r8   r>   r@   rm      s8    
	rm   c                   @      e Zd ZdS )Olmo2RotaryEmbeddingNrA   rB   rC   r8   r8   r8   r@   ru   1      ru   c                   @   rt   )Olmo2PreTrainedModelNrv   r8   r8   r8   r@   rx   5  rw   rx   c                       s"   e Zd Zdef fddZ  ZS )
Olmo2ModelrU   c                    sB   t    t j jd| _t fddt j	D | _
d S )Nrn   c                    s   g | ]}t  |qS r8   )rm   ).0rV   rU   r8   r@   
<listcomp>@  s    z'Olmo2Model.__init__.<locals>.<listcomp>)r9   r:   rF   r'   r;   r   nnZ
ModuleListranger)   r   )r<   rU   r>   r{   r@   r:   <  s
   
zOlmo2Model.__init__)rA   rB   rC   r   r:   rE   r8   r8   r>   r@   ry   ;  s    ry   c                   @   rt   )Olmo2ForCausalLMNrv   r8   r8   r8   r@   r   E  rw   r   )r   r   ry   rx   )*typingr   r   r   rI   Ztorch.nnr}   Zcache_utilsr   Zmodeling_utilsr   Zpytorch_utilsr   utilsr	   Zllama.modeling_llamar   r   r   Zolmo.configuration_olmor   Zolmo.modeling_olmor   r   r   r   r   r   Z
get_loggerrA   rh   r   rF   appendrS   rT   rm   ru   rx   ry   r   __all__r8   r8   r8   r@   <module>   s.     

 
	
?2
