Ë
    ÛÓ^jôe  ã                   óº  — d dl Z 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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! 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l0m1Z1  G d„ dejd                  «      Z3 G d„ dejd                  «      Z4d„ Z5 ed«      d?d„«       Z6dejn                  de8dejn                  fd „Z9	 d@d!ejd                  d"ejn                  d#ejn                  d$ejn                  d%ejn                  dz  d&e:d'e:d(e&e(   fd)„Z;d*„ Z< ee6«       G d+„ d,ejd                  «      «       Z= ed-«       G d.„ d/ejd                  «      «       Z> G d0„ d1e«      Z?e) G d2„ d3e$«      «       Z@e) G d4„ d5e@«      «       ZAe) G d6„ d7e@e«      «       ZB G d8„ d9ee@«      ZC G d:„ d;ee@«      ZD G d<„ d=ee@«      ZEg d>¢ZFy)Aé    N)ÚCallable)ÚOptional)Únné   )Úinitialization)ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_forward_from_hubÚuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_mask)ÚGenericForQuestionAnsweringÚ GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstringÚcan_return_tuple)Úmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )ÚDiffLlamaConfigc                   ó$   ‡ — e Zd Zˆ fd„Zd„ Zˆ xZS )ÚDiffLlamaMLPc                 óÊ  •— t         ‰| �  «        || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _        t        j                  | j                  | j                  d¬«      | _	        t        |j                     | _        y ©NF©Úbias)ÚsuperÚ__init__ÚconfigÚhidden_sizeÚintermediate_sizer   ÚLinearÚ	gate_projÚup_projÚ	down_projr   Ú
hidden_actÚact_fn©Úselfr+   Ú	__class__s     €ú{/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/diffllama/modeling_diffllama.pyr*   zDiffLlamaMLP.__init__5   s¦   ø€ Ü‰ÑÔØˆŒØ!×-Ñ-ˆÔØ!'×!9Ñ!9ˆÔÜŸ™ 4×#3Ñ#3°T×5KÑ5KÐRWÔXˆŒÜ—y‘y ×!1Ñ!1°4×3IÑ3IÐPUÔVˆŒÜŸ™ 4×#9Ñ#9¸4×;KÑ;KÐRWÔXˆŒÜ˜V×.Ñ.Ñ/ˆ�ó    c                 óˆ   — | j                  | j                  | j                  |«      «      | j                  |«      z  «      }|S ©N)r1   r3   r/   r0   )r5   Úxr1   s      r7   ÚforwardzDiffLlamaMLP.forward?   s6   € Ø—N‘N 4§;¡;¨t¯~©~¸aÓ/@Ó#AÀDÇLÁLÐQRÃOÑ#SÓTˆ	ØÐr8   )Ú__name__Ú
__module__Ú__qualname__r*   r<   Ú__classcell__©r6   s   @r7   r$   r$   4   s   ø„ ô0ör8   r$   c                   óÄ   ‡ — e Zd ZU ej                  ed<   ddefˆ 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ˆ xZS )ÚDiffLlamaRotaryEmbeddingÚinv_freqNr+   c                 ó®  •— t         ‰| �  «        |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |«      \  }| _
        | j                  d|d¬«       | j                  d|j                  «       d¬«       y )NÚ	rope_typeÚdefaultrD   F)Ú
persistentÚoriginal_inv_freq)r)   r*   Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr+   Úrope_parametersrF   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)r5   r+   ÚdeviceÚrope_init_fnrD   r6   s        €r7   r*   z!DiffLlamaRotaryEmbedding.__init__G   s¶   ø€ Ü‰ÑÔØ"(×"@Ñ"@ˆÔØ$*×$BÑ$BˆÔ!àˆŒàŸ™×4Ñ4°[ÑAˆŒØ!%×!EÑ!EˆØ�>‰>˜YÒ&Ü.¨t¯~©~Ñ>ˆLÙ+7¸¿¹ÀVÓ+LÑ(ˆ�$Ô(à×Ñ˜Z¨¸eÐÔDØ×ÑÐ0°(·.±.Ó2BÈuÐÕUr8   rR   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 ó  — | 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).
        Ú
rope_thetaÚhead_dimNg      ð?r   é   ©Údtype)rR   r[   )	rM   Úgetattrr,   Únum_attention_headsÚtorchÚarangeÚint64ÚtoÚfloat)r+   rR   rT   ÚbaseÚdimÚattention_factorrD   s          r7   rN   z8DiffLlamaRotaryEmbedding.compute_default_rope_parametersW   s�   € ð& ×%Ñ% lÑ3ˆÜ�f˜j¨$Ó/Òc°6×3EÑ3EÈ×IcÑIcÑ3cˆàÐð Ø”U—\‘\ ! S¨!´5·;±;Ô?×BÑBÈ&ÔX]×XcÑXcÐBÓdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r8   c                 óN  — | j                   d d d …d f   j                  «       j                  |j                  d   dd«      j	                  |j
                  «      }|d d …d d d …f   j                  «       }t        |j
                  j                  t        «      r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d¬«      5  |j                  «       |j                  «       z  j                  dd«      }t        j                  ||fd¬	«      }|j                  «       | j                  z  }|j                  «       | j                  z  }	d d d «       j	                  |j                   ¬
«      	j	                  |j                   ¬
«      fS # 1 sw Y   ŒAxY w)Nr   éÿÿÿÿr!   ÚmpsÚcpuF)Údevice_typeÚenabledrY   ©rd   rZ   )rD   rb   ÚexpandÚshapera   rR   Ú
isinstanceÚtypeÚstrr   Ú	transposer^   ÚcatÚcosrO   Úsinr[   )
r5   r;   Úposition_idsÚinv_freq_expandedÚposition_ids_expandedrj   ÚfreqsÚembrt   ru   s
             r7   r<   z DiffLlamaRotaryEmbedding.forwardu   sR  € ð !ŸM™M¨$²°4¨-Ñ8×>Ñ>Ó@×GÑGÈ×HZÑHZÐ[\ÑH]Ð_aÐcdÓe×hÑhÐij×iqÑiqÓrÐØ ,ªQ°²a¨ZÑ 8× >Ñ >Ó @Ðä'1°!·(±(·-±-ÄÔ'EÈ!Ï(É(Ï-É-Ð[`ÒJ`�a—h‘h—m’mÐfkˆÜ¨¸UÔCñ 	5Ø&×,Ñ,Ó.Ð1F×1LÑ1LÓ1NÑN×YÑYÐZ[Ð]^Ó_ˆEÜ—)‘)˜U E˜N°Ô3ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆCØ—'‘'“)˜d×4Ñ4Ñ4ˆC÷		5ð �v‰v˜AŸG™GˆvÓ$ c§f¡f°1·7±7 fÓ&;Ð;Ð;÷	5ð 	5ús   ÃBFÆF$r:   )NNN)r=   r>   r?   r^   ÚTensorÚ__annotations__r"   r*   Ústaticmethodr   ÚintÚtuplerb   rN   Úno_gradr   r<   r@   rA   s   @r7   rC   rC   D   s˜   ø… Ø�l‰lÓñV˜õ Vð  à)-Ø+/Ø"ñ*Ø $Ñ&ð*à˜Ñ(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ñ	%ò	*ó ð*ð: €U‡]�]ƒ_Øñ<ó ó ô<r8   rC   c                 óš   — | 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..Nrg   rY   rl   )rn   r^   rs   )r;   Úx1Úx2s      r7   Úrotate_halfr„   …   sZ   € à	
ˆ3Ð"�!—'‘'˜"‘+ Ñ"Ð"Ð"Ñ	#€BØ	
ˆ3�—‘˜‘˜qÑ Ñ"Ð"Ñ	#€BÜ�9‰9�r�c˜2�Y BÔ'Ð'r8   Úrotary_pos_embc                 óž   — |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.
    )Ú	unsqueezer„   )ÚqÚkrt   ru   Úunsqueeze_dimÚq_embedÚk_embeds          r7   Úapply_rotary_pos_embr�   Œ   sY   € ð& �-‰-˜Ó
&€CØ
�-‰-˜Ó
&€CØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�3‰wœ; q›>¨CÑ/Ñ0€GØ�GÐÐr8   Úhidden_statesÚn_reprU   c                 óª   — | j                   \  }}}}|dk(  r| S | dd…dd…ddd…dd…f   j                  |||||«      } | j                  |||z  ||«      S )zÔ
    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)rn   rm   Úreshape)rŽ   r�   ÚbatchÚnum_key_value_headsÚslenrX   s         r7   Ú	repeat_kvr•   ¦   so   € ð
 2?×1DÑ1DÑ.€EÐ  hØ�‚zØÐØ!¢!¢Q¨ªa²Ð"2Ñ3×:Ñ:¸5ÐBUÐW\Ð^bÐdlÓm€MØ× Ñ  Ð(;¸eÑ(CÀTÈ8ÓTÐTr8   ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t        || j                  «      }t        || j                  «      }	t        j                  ||j	                  dd«      «      |z  }
|�|
|z   }
t
        j                  j                  |
dt        j                  ¬«      j                  |j                  «      }
t
        j                  j                  |
|| j                  ¬«      }
t        j                  |
|	«      }|j	                  dd«      j                  «       }||
fS )NrY   r   rg   ©rd   r[   )ÚpÚtrainingr!   )r•   Únum_key_value_groupsr^   Úmatmulrr   r   Ú
functionalÚsoftmaxÚfloat32ra   r[   rœ   r¡   Ú
contiguous)r–   r—   r˜   r™   rš   r›   rœ   r�   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r7   Úeager_attention_forwardr¬   ²   sà   € ô ˜3 × ;Ñ ;Ó<€JÜ˜U F×$?Ñ$?Ó@€Lä—<‘<  z×';Ñ';¸A¸qÓ'AÓBÀWÑL€LØÐ!Ø# nÑ4ˆä—=‘=×(Ñ(¨¸2ÄUÇ]Á]Ð(ÓS×VÑVÐW\×WbÑWbÓc€LÜ—=‘=×(Ñ(¨¸È6Ï?É?Ð(Ó[€LÜ—,‘,˜|¨\Ó:€KØ×'Ñ'¨¨1Ó-×8Ñ8Ó:€Kà˜Ð$Ð$r8   c                 ó>   — ddt        j                  d| z  «      z  z
  S )Ngš™™™™™é?g333333ã?g333333Ó¿)ÚmathÚexp)Ú	layer_idxs    r7   Úlambda_init_fnr±   Ë   s    € Ø�”t—x‘x  yÑ 0Ó1Ñ1Ñ1Ð1r8   c                   óî   ‡ — e Zd ZdZddededz  fˆ 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j                  ej                  f   f
d„Zˆ xZS )ÚDiffLlamaAttentionuÿ  Multi-headed differential attention (https://huggingface.co/papers/2410.05258).

    Computes ``(softmax(Q1 K1áµ€) - Î» Â· softmax(Q2 K2áµ€)) Â· V`` as two standard attention calls
    sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
    V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
    forces SDPA off Flash/cuDNN onto the memory-efficient/math kernel; Flash Attention 2 also
    requires ``head_dim_v == head_dim_q``.
    Nr+   r°   c                 ó¶  •— 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                  ¬«      | _        |j                  dkD  rt)        d«      ‚|j                  �|j                  dz  dk7  rt)        d	|j                  › d
�«      ‚t+        |«      | _        t        j.                  t1        j2                  d|j4                  | j                  f¬«      «      | _        t        j.                  t1        j2                  d|j4                  | j                  f¬«      «      | _        t        j.                  t1        j2                  d|j4                  | j                  f¬«      «      | _        t        j.                  t1        j2                  d|j4                  | j                  f¬«      «      | _        t        j>                  d| j                  z  |j@                  d¬«      | _!        y )NrX   g      à¿Tr'   ç        z€DiffLlama does not support `attention_dropout > 0`: the differential attention mechanism has no paper-defined dropout semantics.rY   r   z–DiffLlama requires `num_key_value_heads` to be even (and at least 2): the two-call differential attention splits the value tensor along KV heads, got ú.)ÚsizeF)ÚepsÚelementwise_affine)"r)   r*   r+   r°   r\   r,   r]   rX   r“   r¢   r›   Úattention_dropoutÚ	is_causalr   r.   Úattention_biasÚq_projÚk_projÚv_projÚo_projÚ
ValueErrorr±   Úlambda_initÚ	Parameterr^   ÚnormalÚlambda_std_devÚ	lambda_q1Ú	lambda_k1Ú	lambda_q2Ú	lambda_k2ÚRMSNormÚrms_norm_epsÚ	groupnorm©r5   r+   r°   r6   s      €r7   r*   zDiffLlamaAttention.__init__Ú   s   ø€ Ü‰ÑÔØˆŒØ"ˆŒÜ ¨
°F×4FÑ4FÈ&×JdÑJdÑ4dÓeˆŒØ$*×$>Ñ$>À&×B\ÑB\Ñ$\ˆÔ!Ø—}‘} dÑ*ˆŒØ!'×!9Ñ!9ˆÔØˆŒä—i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×Ñ × :Ñ :¸T¿]¹]Ñ JÐQW×QfÑQfô
ˆŒô —i‘iØ×&Ñ&¨¯©Ñ6¸×8JÑ8JÐQW×QfÑQfô
ˆŒð ×#Ñ# cÒ)ÜðDóð ð ×%Ñ%Ð-°×1KÑ1KÈaÑ1OÐSTÒ1TÜðVØV\×VpÑVpÐUqÐqrðtóð ô *¨)Ó4ˆÔÜŸ™¤e§l¡l°1°f×6KÑ6KÐSW×S`ÑS`ÐRbÔ&cÓdˆŒÜŸ™¤e§l¡l°1°f×6KÑ6KÐSW×S`ÑS`ÐRbÔ&cÓdˆŒÜŸ™¤e§l¡l°1°f×6KÑ6KÐSW×S`ÑS`ÐRbÔ&cÓdˆŒÜŸ™¤e§l¡l°1°f×6KÑ6KÐSW×S`ÑS`ÐRbÔ&cÓdˆŒÜŸ™ A¨¯©Ñ$5¸6×;NÑ;NÐchÔiˆ�r8   rŽ   Úposition_embeddingsrš   Úpast_key_valuesrU   c                 ó¾  — |j                   d d }g |¢d‘| j                  ‘­}| j                  |«      j                  |«      j	                  dd«      }| j                  |«      j                  |«      j	                  dd«      }	| j                  |«      j                  |«      j	                  dd«      }
|\  }}t        ||	||«      \  }}	|� |j                  |	|
| j                  «      \  }	}
d„ t        j                  |
dd¬«      D «       \  }}t        j                  | j                  j                  t         «      } || ||	||fd| j"                  dœ|¤Ž\  }} || ||	||fd| j"                  dœ|¤Ž\  }}t        j$                  ||gd¬«      }t        j                  |dd¬«      \  }}t        j&                  t        j(                  | j*                  | j,                  z  dt        j.                  ¬«      «      j1                  |j2                  «      }t        j&                  t        j(                  | j4                  | j6                  z  dt        j.                  ¬«      «      j1                  |j2                  «      }||z
  | j8                  z   }|||z  z
  }d| j8                  z
  | j;                  |«      z  } |j<                  g |¢d‘­Ž }| j?                  |«      }||fS )	Nrg   r!   rY   c              3   óD   K  — | ]  }|j                  d dd d «      –— Œ y­w)r!   rY   N)Úrepeat)Ú.0Úvs     r7   ú	<genexpr>z-DiffLlamaAttention.forward.<locals>.<genexpr>  s   è ø€ Ò'jÀ¨¯©°°A°q¸!×(<Ñ'jùs   ‚ rl   rµ   )rœ   r›   rŸ   ) rn   rX   r½   Úviewrr   r¾   r¿   r�   Úupdater°   r^   Úchunkr   Úget_interfacer+   Ú_attn_implementationr¬   r›   rs   r¯   ÚsumrÆ   rÇ   r¦   ra   r[   rÈ   rÉ   rÂ   rÌ   r‘   rÀ   )r5   rŽ   rÎ   rš   rÏ   r�   Úinput_shapeÚhidden_shapeÚquery_statesr¨   r©   rt   ru   Úvalue_states1Úvalue_states2Úattention_interfaceÚattn_output1rª   Úattn_output2Ú_r«   Úlambda_1Úlambda_2Úlambda_fulls                           r7   r<   zDiffLlamaAttention.forward  sÐ  € ð $×)Ñ)¨#¨2Ð.ˆØ8˜Ð8 bÐ8¨$¯-©-Ñ8ˆà—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆØ—[‘[ Ó/×4Ñ4°\ÓB×LÑLÈQÐPQÓRˆ
Ø—{‘{ =Ó1×6Ñ6°|ÓD×NÑNÈqÐRSÓTˆà&‰ˆˆSÜ#7¸ÀjÐRUÐWZÓ#[Ñ ˆ�jàÐ&Ø'6×'=Ñ'=¸jÈ,ÐX\×XfÑXfÓ'gÑ$ˆJ˜ñ (kÄeÇkÁkÐR^Ð`aÐghÔFiÔ'jÑ$ˆ�}ä(?×(MÑ(MØ�K‰K×,Ñ,Ô.Eó)
Ðñ &9ØØØØØð	&
ð Ø—L‘Lñ	&
ð ñ	&
Ñ"ˆ�lñ .ØØØØØð	
ð Ø—L‘Lñ	
ð ñ	
‰ˆ�aô —i‘i ¨|Ð <À"ÔEˆô &+§[¡[°¸aÀQÔ%GÑ"ˆ�lÜ—9‘9œUŸY™Y t§~¡~¸¿¹Ñ'FÈBÔV[×VcÑVcÔdÓe×hÑhØ×Ñó
ˆô —9‘9œUŸY™Y t§~¡~¸¿¹Ñ'FÈBÔV[×VcÑVcÔdÓe×hÑhØ×Ñó
ˆð  Ñ)¨D×,<Ñ,<Ñ<ˆØ" [°<Ñ%?Ñ?ˆØ˜4×+Ñ+Ñ+¨t¯~©~¸kÓ/JÑJˆØ)�k×)Ñ)Ð;¨;Ð;¸Ò;ˆØ—k‘k +Ó.ˆØ˜LÐ(Ð(r8   r:   )NN)r=   r>   r?   Ú__doc__r"   r~   r*   r^   r{   r   r	   r<   r@   rA   s   @r7   r³   r³   Ï   s›   ø„ ññ+j˜ð +j¸3À¹:õ +jðb /3Ø(,ñC)à—|‘|ðC)ð # 5§<¡<°·±Ð#=Ñ>ðC)ð Ÿ™ tÑ+ð	C)ð
  ™ðC)ð 
ˆu�|‰|˜UŸ\™\Ð)Ñ	*÷C)r8   r³   rÊ   c                   óh   ‡ — e Zd Zddeddfˆ fd„Zdej                  dej                  fd„Zd„ Zˆ xZ	S )	ÚDiffLlamaRMSNormr¸   rU   Nc                 óŠ   •— t         ‰| �  «        t        j                  t	        j
                  |«      «      | _        || _        y)z?
        DiffLlamaRMSNorm is equivalent to T5LayerNorm
        N)r)   r*   r   rÃ   r^   ÚonesÚweightÚvariance_epsilon)r5   r,   r¸   r6   s      €r7   r*   zDiffLlamaRMSNorm.__init__O  s1   ø€ ô 	‰ÑÔÜ—l‘l¤5§:¡:¨kÓ#:Ó;ˆŒØ #ˆÕr8   rŽ   c                 ó"  — |j                   }|j                  t        j                  «      }|j	                  d«      j                  dd¬«      }|t        j                  || j                  z   «      z  }| j                  |j                  |«      z  S )NrY   rg   T)Úkeepdim)	r[   ra   r^   r¦   ÚpowÚmeanÚrsqrtrî   rí   )r5   rŽ   Úinput_dtypeÚvariances       r7   r<   zDiffLlamaRMSNorm.forwardW  sy   € Ø#×)Ñ)ˆØ%×(Ñ(¬¯©Ó7ˆØ ×$Ñ$ QÓ'×,Ñ,¨R¸Ð,Ó>ˆØ%¬¯©°H¸t×?TÑ?TÑ4TÓ(UÑUˆØ�{‰{˜]×-Ñ-¨kÓ:Ñ:Ð:r8   c                 ó^   — t        | j                  j                  «      › d| j                  › �S )Nz, eps=)r   rí   rn   rî   )r5   s    r7   Ú
extra_reprzDiffLlamaRMSNorm.extra_repr^  s*   € Ü˜Ÿ™×)Ñ)Ó*Ð+¨6°$×2GÑ2GÐ1HÐIÐIr8   )g�íµ ÷Æ°>)
r=   r>   r?   rb   r*   r^   r{   r<   r÷   r@   rA   s   @r7   rê   rê   M  s7   ø„ ñ$¨ð $¸$õ $ð; U§\¡\ð ;°e·l±ló ;öJr8   rê   c                   ó  ‡ — e Zd Zdedefˆ fd„Z	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   dej                  fd„Zˆ xZS )ÚDiffLlamaDecoderLayerr+   r°   c                 ó   •— t         ‰| �  «        |j                  | _        t        ||¬«      | _        t        |«      | _        t        |j                  |j                  ¬«      | _	        t        |j                  |j                  ¬«      | _
        y )N)r+   r°   ©r¸   )r)   r*   r,   r³   Ú	self_attnr$   Úmlprê   rË   Úinput_layernormÚpost_attention_layernormrÍ   s      €r7   r*   zDiffLlamaDecoderLayer.__init__c  sm   ø€ Ü‰ÑÔØ!×-Ñ-ˆÔä+°6ÀYÔOˆŒä Ó'ˆŒÜ/°×0BÑ0BÈ×H[ÑH[Ô\ˆÔÜ(8¸×9KÑ9KÐQW×QdÑQdÔ(eˆÕ%r8   NrŽ   rš   rv   rÏ   Ú	use_cacherÎ   r�   rU   c           
      óÀ   — |}| j                  |«      } | j                  d||||||dœ|¤Ž\  }}	||z   }|}| j                  |«      }| j                  |«      }||z   }|S )N)rŽ   rš   rv   rÏ   r   rÎ   © )rþ   rü   rÿ   rý   )
r5   rŽ   rš   rv   rÏ   r   rÎ   r�   Úresidualrä   s
             r7   r<   zDiffLlamaDecoderLayer.forwardm  s�   € ð !ˆØ×,Ñ,¨]Ó;ˆà)˜4Ÿ>™>ð 
Ø'Ø)Ø%Ø+ØØ 3ñ
ð ñ
Ñˆ�qð ! =Ñ0ˆð !ˆØ×5Ñ5°mÓDˆØŸ™ Ó/ˆØ  =Ñ0ˆØÐr8   )NNNFN)r=   r>   r?   r"   r~   r*   r^   r{   Ú
LongTensorr	   Úboolr   r   r   r<   r@   rA   s   @r7   rù   rù   b  s¾   ø„ ðf˜ð f¸3õ fð /3Ø04Ø(,Ø!&ØHLñà—|‘|ðð Ÿ™ tÑ+ðð ×&Ñ&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5§<¡<°·±Ð#=Ñ>ÀÑEðð Ð+Ñ,ðð 
�‰÷r8   rù   c                   ó„   ‡ — e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedœZ ej$                  «       ˆ fd„«       Zˆ xZS )ÚDiffLlamaPreTrainedModelr+   ÚmodelTrù   rÏ   )rŽ   Ú
attentionsc                 óî  •— t         ‰| �  |«       t        |t        «      rÕt	        j
                  |j                  d| j                  j                  «       t	        j
                  |j                  d| j                  j                  «       t	        j
                  |j                  d| j                  j                  «       t	        j
                  |j                  d| j                  j                  «       y y )Nr   )r)   Ú_init_weightsro   r³   ÚinitÚnormal_rÆ   r+   rÅ   rÇ   rÈ   rÉ   )r5   r–   r6   s     €r7   r  z&DiffLlamaPreTrainedModel._init_weightsŸ  s    ø€ ä‰Ñ˜fÔ%Ü�fÔ0Ô1Ü�L‰L˜×)Ñ)¨1¨d¯k©k×.HÑ.HÔIÜ�L‰L˜×)Ñ)¨1¨d¯k©k×.HÑ.HÔIÜ�L‰L˜×)Ñ)¨1¨d¯k©k×.HÑ.HÔIÜ�L‰L˜×)Ñ)¨1¨d¯k©k×.HÑ.HÕIð	 2r8   )r=   r>   r?   r"   r|   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrù   r³   Ú_can_record_outputsr^   r€   r  r@   rA   s   @r7   r  r  �  sp   ø… àÓØÐØ&*Ð#Ø0Ð1ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà.Ø(ñÐð
 €U‡]�]ƒ_óJó ôJr8   r  c                   óò   ‡ — e Zd Zdefˆ fd„Ze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dz  d
ee   defd„«       «       «       Zˆ xZS )ÚDiffLlamaModelr+   c           	      ó  •— t         ‰| �  |«       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                  «      | _        t        j                  t        |j                  «      D �cg c]  }t        ||«      ‘Œ c}«      | _        t        |j                  |j                  ¬«      | _        t#        |¬«      | _        d| _        | j)                  «        y c c}w )Nrû   ©r+   F)r)   r*   Úpad_token_idÚpadding_idxÚ
vocab_sizer   Ú	Embeddingr,   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersrù   Úlayersrê   rË   ÚnormrC   Ú
rotary_embÚgradient_checkpointingÚ	post_initrÍ   s      €r7   r*   zDiffLlamaModel.__init__«  sÆ   ø€ Ü‰Ñ˜Ô Ø!×.Ñ.ˆÔØ ×+Ñ+ˆŒäŸL™L¨×):Ñ):¸F×<NÑ<NÐPT×P`ÑP`ÓaˆÔÜ—m‘mÜGLÈV×MeÑMeÓGfÖg¸)Ô" 6¨9Õ5Ògó
ˆŒô % V×%7Ñ%7¸V×=PÑ=PÔQˆŒ	Ü2¸&ÔAˆŒØ&+ˆÔ#ð 	�‰Õùò hs   ÂDNÚ	input_idsrš   rv   rÏ   Úinputs_embedsr   r�   rU   c           
      ó<  — |d u |d uz  rt        d«      ‚|€| j                  |«      }|r|€t        | j                  ¬«      }|€V|�|j	                  «       nd}t        j                  |j                  d   |j                  ¬«      |z   }|j                  d«      }t        | j                  ||||¬«      }	|}
| j                  |
|¬«      }| j                  d | j                  j                   D ]  } ||
f|	||||dœ|¤Ž}
Œ | j                  |
«      }
t        |
|¬	«      S )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r!   )rR   )r+   r*  rš   rÏ   rv   )rv   )rš   rÎ   rv   rÏ   r   )Úlast_hidden_staterÏ   )rÁ   r   r
   r+   Úget_seq_lengthr^   r_   rn   rR   r‡   r   r&  r$  r#  r%  r   )r5   r)  rš   rv   rÏ   r*  r   r�   Úpast_seen_tokensÚcausal_maskrŽ   rÎ   Údecoder_layers                r7   r<   zDiffLlamaModel.forward»  sL  € ð ˜Ð -°tÐ";Ò<ÜÐYÓZÐZàÐ Ø*.×*;Ñ*;¸IÓ*FˆMá˜Ð0Ü*°$·+±+Ô>ˆOàÐØCRÐC^˜×=Ñ=Ô?ÐdeÐÜ Ÿ<™<¨×(;Ñ(;¸AÑ(>À}×G[ÑG[Ô\Ð_oÑoˆLØ'×1Ñ1°!Ó4ˆLä(Ø—;‘;Ø'Ø)Ø+Ø%ô
ˆð &ˆØ"Ÿo™o¨mÈ,˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÐIò 		ˆMÙ)Øðà*Ø$7Ø)Ø /Ø#ñð ñ‰Mð		ð Ÿ	™	 -Ó0ˆÜ&Ø+Ø+ô
ð 	
r8   )NNNNNN)r=   r>   r?   r"   r*   r   r    r   r^   r  r{   r	   ÚFloatTensorr  r   r   r   r<   r@   rA   s   @r7   r  r  ©  sÍ   ø„ ð˜õ ð   ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ñ2
à×#Ñ# dÑ*ð2
ð Ÿ™ tÑ+ð2
ð ×&Ñ&¨Ñ-ð	2
ð
  ™ð2
ð ×(Ñ(¨4Ñ/ð2
ð ˜$‘;ð2
ð Ð+Ñ,ð2
ð 
!ò2
ó ó ó  ô2
r8   r  c                   óB  ‡ — e Zd ZddiZddiZddgdgfiZˆ 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ˆ xZS )ÚDiffLlamaForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrŽ   Úlogitsc                 óè   •— t         ‰| �  |«       t        |«      | _        |j                  | _        t        j                  |j                  |j                  d¬«      | _        | j                  «        y r&   )
r)   r*   r  r  r  r   r.   r,   r4  r(  r4   s     €r7   r*   zDiffLlamaForCausalLM.__init__ù  sU   ø€ Ü‰Ñ˜Ô Ü# FÓ+ˆŒ
Ø ×+Ñ+ˆŒÜ—y‘y ×!3Ñ!3°V×5FÑ5FÈUÔSˆŒð 	�‰Õr8   Nr)  rš   rv   rÏ   r*  Úlabelsr   Úlogits_to_keepr�   rU   c	           
      óx  —  | j                   d||||||dœ|	¤Ž}
|
j                  }t        |t        «      rt	        | d«      n|}| j                  |dd…|dd…f   «      }d}|�* | j                  d||| j                  j                  dœ|	¤Ž}t        |||
j                  |
j                  |
j                  ¬«      S )aŒ  
        Example:

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

        >>> model = DiffLlamaForCausalLM.from_pretrained("google/diffllama-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/diffllama-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```)r)  rš   rv   rÏ   r*  r   N)r6  r8  r  )Úlossr6  rÏ   rŽ   r	  r  )r  r,  ro   r~   Úslicer4  Úloss_functionr+   r  r   rÏ   rŽ   r	  )r5   r)  rš   rv   rÏ   r*  r8  r   r9  r�   ÚoutputsrŽ   Úslice_indicesr6  r;  s                  r7   r<   zDiffLlamaForCausalLM.forward  sÙ   € ð> ,6¨4¯:©:ð ,
ØØ)Ø%Ø+Ø'Øñ,
ð ñ,
ˆð  ×1Ñ1ˆä8BÀ>ÔSVÔ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜mªA¨}ºaÐ,?Ñ@ÓAˆàˆØÐØ%�4×%Ñ%Ðp¨V¸FÈtÏ{É{×OeÑOeÑpÐioÑpˆDä%ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r8   )NNNNNNNr   )r=   r>   r?   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr*   r   r   r^   r  r{   r	   r1  r  r~   r   r   r   r<   r@   rA   s   @r7   r3  r3  ó  s  ø„ à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hôð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ñ6
à×#Ñ# dÑ*ð6
ð Ÿ™ tÑ+ð6
ð ×&Ñ&¨Ñ-ð	6
ð
  ™ð6
ð ×(Ñ(¨4Ñ/ð6
ð × Ñ  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eŸl™lÑ*ð6
ð Ð+Ñ,ð6
ð 
 ò6
ó ó ô6
r8   r3  c                   ó   — e Zd Zy)Ú"DiffLlamaForSequenceClassificationN©r=   r>   r?   r  r8   r7   rD  rD  =  ó   „ Ør8   rD  c                   ó   — e Zd ZdZy)ÚDiffLlamaForQuestionAnsweringÚtransformerN)r=   r>   r?   r  r  r8   r7   rH  rH  A  s   „ Ø%Ñr8   rH  c                   ó   — e Zd Zy)ÚDiffLlamaForTokenClassificationNrE  r  r8   r7   rK  rK  E  rF  r8   rK  )r  r  r3  rD  rH  rK  )r!   )rµ   )Gr®   Úcollections.abcr   Útypingr   r^   r   Ú r   r  Úactivationsr   Úcache_utilsr	   r
   Ú
generationr   Úintegrationsr   r   r   Úmasking_utilsr   Úmodeling_layersr   r   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   r   Úutils.output_capturingr    Úconfiguration_diffllamar"   ÚModuler$   rC   r„   r�   r{   r~   r•   rb   r¬   r±   r³   rê   rù   r  r  r3  rD  rH  rK  Ú__all__r  r8   r7   ú<module>r_     s(  ðó. Ý $Ý ã Ý å &Ý !ß .Ý )ß fÑ fÝ /÷ó ÷ Pß Kß FÝ &ß IÑ Iß GÝ 5Ý 4ô�2—9‘9ô ô ><˜rŸy™yô ><òB(ñ Ð*Ó+òó ,ðð2	U˜UŸ\™\ð 	U°#ð 	U¸%¿,¹,ó 	Uð& ñ%Ø�I‰Ið%à�<‰<ð%ð 
�‰ð%ð �<‰<ð	%ð
 —L‘L 4Ñ'ð%ð ð%ð ð%ð Ð'Ñ(ó%ò22ñ Ð)Ó*ôz)˜Ÿ™ó z)ó +ðz)ñz ˜YÓ'ôJ�r—y‘yó Jó (ðJô((Ð6ô (ðV ôJ˜ó Jó ðJð6 ôF
Ð-ó F
ó ðF
ðR ôF
Ð3°_ó F
ó ðF
ôR	Ð)IÐKcô 	ô&Ð$?ÐAYô &ô	Ð&CÐE]ô 	ò�r8   