
    fThg                     J   S SK JrJrJrJrJr  S SKrS SKJs  J	r
  S SKJr  SSKJr  SSKJrJr  SSKJr  SSKJr  SS	KJr  SS
KJrJrJr  SSKJrJr  SSKJrJr  SSK J!r!J"r"J#r#  SSK$J%r%  \"" 5       (       a  S SK&J'r'  SSK(J)r)  \#RT                  " \+5      r, " S S\RZ                  5      r. " S S\RZ                  5      r/ " S S\RZ                  5      r0 " S S\RZ                  5      r1 " S S\RZ                  5      r2S r3S:S jr4S\Rj                  S\6S \Rj                  4S! jr7 S;S"\RZ                  S#\Rj                  S$\Rj                  S%\Rj                  S&\\Rj                     S'\8S(\84S) jjr9 " S* S+\RZ                  5      r: " S, S-\5      r;\! " S. S/\5      5       r< " S0 S1\RZ                  5      r=\! " S2 S3\<5      5       r>   S<S4\\Rj                  \\Rj                     S4   S5\\6   S&\\Rj                     S \\Rj                  \64   4S6 jjr? " S7 S8\<\5      r@/ S9QrAg)=    )CallableListOptionalTupleUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)AttentionMaskConverter)GradientCheckpointingLayer)BaseModelOutputWithPastMoeCausalLMOutputWithPastMoeModelOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)auto_docstringis_torch_flex_attn_availablelogging   )GraniteMoeSharedConfig)	BlockMask)make_flex_block_causal_maskc                   n   ^  \ rS rSrSrS\4U 4S jjrS\R                  S\R                  4S jr	Sr
U =r$ )	GraniteMoeSharedMLP1   zj
MLP layer for shared experts

Args:
    config:
        Configuration object with model hyperparameters.
configc                 `  > [         [        U ]  5         UR                  U l        UR
                  U l        [        UR                     U l        [        R                  " U R                  U R                  S-  SS9U l        [        R                  " U R                  U R                  SS9U l        g )N   Fbias)superr   __init__hidden_size
input_sizeshared_intermediate_sizer
   
hidden_act
activationr   Linearinput_linearoutput_linearselfr!   	__class__s     v/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/granitemoeshared/modeling_granitemoeshared.pyr'   GraniteMoeSharedMLP.__init__:   s    !413 ,,!:: !2!23IIdoot7G7G!7KRWXYYt'7'7uU    hidden_statesreturnc                     U R                  U5      nUR                  SSS9nU R                  US   5      US   -  nU R                  U5      nU$ )Nr#   dimr   r   )r.   chunkr,   r/   )r1   r6   chunked_hidden_statess      r3   forwardGraniteMoeSharedMLP.forwardC   s^    ))-8 - 3 3A2 3 >(=a(@ADYZ[D\\**=9r5   )r,   r(   r.   r)   r/   )__name__
__module____qualname____firstlineno____doc__r   r'   torchTensorr>   __static_attributes____classcell__r2   s   @r3   r   r   1   s7    V5 VU\\ ell  r5   r   c                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )GraniteMoeSharedRMSNormK   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z6
GraniteMoeSharedRMSNorm is equivalent to T5LayerNorm
N)r&   r'   r   	ParameterrE   onesweightvariance_epsilon)r1   r(   epsr2   s      r3   r'    GraniteMoeSharedRMSNorm.__init__L   s/     	ll5::k#:; #r5   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr#   r9   T)keepdim)	dtypetorE   float32powmeanrsqrtrQ   rP   )r1   r6   input_dtypevariances       r3   r>   GraniteMoeSharedRMSNorm.forwardT   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r5   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)tuplerP   shaperQ   r1   s    r3   
extra_repr"GraniteMoeSharedRMSNorm.extra_repr[   s*    ))*+6$2G2G1HIIr5   )rQ   rP   )gư>)	r@   rA   rB   rC   r'   r>   rc   rG   rH   rI   s   @r3   rK   rK   K   s    $;J Jr5   rK   c                   B   ^  \ rS rSrS\S\S\SS4U 4S jjrS rS	rU =r$ )
GraniteMoeSharedParallelExperts_   num_expertsr)   output_sizer7   Nc                    > [         TU ]  5         [        R                  " [        R
                  " XU5      5      U l        Xl        X l        X0l	        g)a]  
Initialize the GraniteMoeSharedParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's compatible with
many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
used in vllm.

Args:
    num_experts (int):
        Number of experts.
    input_size (int):
        Size of the input.
    output_size (int):
        Size of the output.
N)
r&   r'   r   rN   rE   emptyrP   rh   r)   ri   )r1   rh   r)   ri   r2   s       r3   r'   (GraniteMoeSharedParallelExperts.__init__`   s<    " 	ll5;;{#TU&$&r5   c                     UR                  USS9n/ n[        U R                  5       H8  nUR                  [        R
                  " X5   U R                  U   5      5        M:     [        R                  " USS9nU$ )z
Forward pass of the GraniteMoeSharedParallelExperts module.

Args:
    inputs (Tensor):
        Input tensor.
    expert_size:
        Expert size information.

Returns:
    Tensor: Output tensor.
r   r:   )	splitrangerh   appendFlinearrP   rE   cat)r1   inputsexpert_size
input_listoutput_listiresultss          r3   r>   'GraniteMoeSharedParallelExperts.forwardw   sh     \\+1\5
t''(Aqxx
t{{1~FG )))KQ/r5   )r)   rh   ri   rP   	r@   rA   rB   rC   intr'   r>   rG   rH   rI   s   @r3   rf   rf   _   s.    'C 'S 's 't '. r5   rf   c                   >   ^  \ rS rSrS\S\S\4U 4S jjrS rSrU =r$ )GraniteMoeSharedTopKGating   r)   rh   top_kc                 z   > [         TU ]  5         X l        Xl        X0l        [
        R                  " XSS9U l        g)z
Initialize the top-k gating mechanism.
Args:
    input_size (`int`):
        Size of the input.
    num_experts (`int`):
        Number of experts.
    top_k (`int`):
        Number of top experts to select.
Fr$   N)r&   r'   rh   r)   r   r   r-   layer)r1   r)   rh   r   r2   s       r3   r'   #GraniteMoeSharedTopKGating.__init__   s2     	&$
YYzUC
r5   c                 z   U R                  U5      R                  5       nUR                  U R                  SS9u  p4[        R
                  " USS9R                  U5      n[        R                  " UR                  S5      U R                  /UR                  UR                  S9nUR                  SUS5      nUR                  5       R                  S5      nUR                  5       nUR!                  5       n	U	R#                  S5      u  pUR%                  U R                  SS9nUR!                  5       nX[   nXXU4$ )Nr   r:   r   rV   devicetrunc)rounding_mode)r   floattopkr   rE   softmaxtype_aszerossizerh   rV   r   scatterlongsumtolistflattensortdiv)r1   r6   logitstop_k_logitstop_k_indicestop_k_gatesr   gatesru   top_k_experts_index_sorted_expertsbatch_indexbatch_gatess                 r3   r>   "GraniteMoeSharedTopKGating.forward   s"   M*002&,kk$**!k&D#mmLa8@@O a $"2"23;;L;LU`UgUg
 a2jjl&&q) "((* &--/"/"4"4Q"7*..tzz.Q "))+!7#+FRRr5   )r)   r   rh   r   r{   rI   s   @r3   r~   r~      s-    D3 DS D D&S Sr5   r~   c                   :   ^  \ rS rSrSrS\4U 4S jjrS rSrU =r	$ )GraniteMoeSharedMoE   z
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.

Args:
    config:
        Configuration object with model hyperparameters.
r!   c                   > [         [        U ]  5         UR                  U l        UR
                  U l        [        UR                     U l        [        UR                  U R                  U R                  S-  5      U l        [        UR                  U R                  U R                  5      U l        [        U R                  UR                  UR                  S9U l        g )Nr#   )r)   rh   r   )r&   r   r'   r(   r)   intermediate_sizer
   r+   r,   rf   num_local_expertsr.   r/   r~   num_experts_per_tokrouterr0   s     r3   r'   GraniteMoeSharedMoE.__init__   s    !413 ,,!33 !2!23;$$doot7G7G!7K
 =$$d&6&6
 100,,
r5   c                    UR                  5       u  p#nUR                  SU5      nU R                  U5      u  pVpxn	X   n
U R                  X5      nUR	                  SSS9nU R                  US   5      US   -  nU R                  X5      nXSS2S4   -  n[        R                  " X#-  U R                  4UR                  UR                  S9nUR                  SXm5      nUR                  X#U R                  5      nX4$ )z
Forward pass of the mixture of experts layer.

Args:
    layer_input (Tensor):
        Input tensor.

Returns:
    Tensor:
        Output tensor.
    Tensor:
        Router logits.
r9   r#   r:   r   r   Nr   )r   reshaper   r.   r<   r,   r/   rE   r   r)   rV   r   	index_addview)r1   layer_inputbszlengthemb_sizer   r   r   ru   router_logitsexpert_inputsr6   r=   expert_outputsr   layer_outputs                   r3   r>   GraniteMoeSharedMoE.forward   s    !, 0 0 2X!))"h7BF++kBZ?-#0))-E - 3 3A2 3 >(=a(@ADYZ[D\\++MG'ag*>>S\4??;>CWCW`n`u`uvq+F#((dooF**r5   )r,   r(   r.   r)   r/   r   )
r@   rA   rB   rC   rD   r   r'   r>   rG   rH   rI   s   @r3   r   r      s    
5 
&+ +r5   r   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " U* U4SS9$ )z*Rotates half the hidden dims of the input..Nr9   r#   r:   )ra   rE   rs   )xx1x2s      r3   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r5   c                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXg4$ )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.
    position_ids (`torch.Tensor`, *optional*):
        Deprecated and unused.
    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.
)	unsqueezer   )qkcossinposition_idsunsqueeze_dimq_embedk_embeds           r3   apply_rotary_pos_embr      sS    ( --
&C
--
&Cw;q>C/0Gw;q>C/0Gr5   r6   n_repr7   c                     U R                   u  p#pEUS:X  a  U $ U SS2SS2SSS2SS24   R                  X#XU5      n U R                  X#U-  XE5      $ )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)ra   expandr   )r6   r   batchnum_key_value_headsslenhead_dims         r3   	repeat_kvr     s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr5   modulequerykeyvalueattention_maskscalingdropoutc                 @   [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub"  US S 2S S 2S S 2S UR
                  S   24   nX-   n
[        R                  R                  U
S[        R                  S9R                  UR                  5      n
[        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nr#   r	   r9   )r;   rV   )ptrainingr   )r   num_key_value_groupsrE   matmul	transposera   r   
functionalr   rX   rW   rV   r   r   
contiguous)r   r   r   r   r   r   r   kwargs
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r3   eager_attention_forwardr   &  s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1 ==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r5   c                   v  ^  \ rS rSrSrSS\S\\   4U 4S jjjr      SS\	R                  S\\	R                     S\\	R                     S	\\   S
\S\\	R                     S\\\	R                  \	R                  4      S\\	R                  \\	R                     \\\	R                        4   4S jjrSrU =r$ )GraniteMoeSharedAttentioniC  z=Multi-headed attention from 'Attention Is All You Need' paperr!   	layer_idxc                 z  > [         TU ]  5         Xl        X l        Uc-  [        R                  SU R                  R                   S35        UR                  U l        UR                  U l	        UR                  U l        U R                  U R                  -  U l        UR                  U l        U R                  U R                  -  U l        SU l        UR                   U l        U R                  U R                  -  U R                  :w  a&  [%        SU R                   SU R                   S35      e[&        R(                  " U R                  U R                  U R                  -  UR*                  S9U l        [&        R(                  " U R                  U R                  U R                  -  UR*                  S9U l        [&        R(                  " U R                  U R                  U R                  -  UR*                  S9U l        [&        R(                  " U R                  U R                  UR*                  S9U l        g )NzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.Tz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).r$   )r&   r'   r!   r   loggerwarning_oncer2   r@   attention_dropoutr(   num_attention_heads	num_headsr   r   r   	is_causalattention_multiplierr   
ValueErrorr   r-   attention_biasq_projk_projv_projo_projr1   r!   r   r2   s      r3   r'   "GraniteMoeSharedAttention.__init__F  s   " !8!8 9 :, , "(!9!9!--33((DNN:#)#=#= $(NNd6N6N$N!22MMDNN*t/?/??QRVRbRbQc$T^^$4B8 
 ii 0 0$..4==2PW]WlWlmii 0 0$2J2JT]]2Zagavavwii 0 0$2J2JT]]2Zagavavwii 0 0$2B2BI^I^_r5   r6   r   r   past_key_value	use_cachecache_positionposition_embeddingsr7   c                 *   UR                  5       u  pnU R                  U5      nU R                  U5      nU R                  U5      nUR	                  XU R
                  U R                  5      R                  SS5      nUR	                  XU R                  U R                  5      R                  SS5      nUR	                  XU R                  U R                  5      R                  SS5      nUb  UOSu  nnUb  [        XUU5      u  pUb$  UXS.nUR                  XU R                  U5      u  p[        nU R                  R                  S:w  ad  U R                  R                  S:X  a-  UR                  SS5      (       a  [         R#                  S	5        O[$        U R                  R                     nU" U UUUU4U R&                  (       d  S
OU R(                  U R*                  S.UD6u  nnUR	                  XS5      nU R-                  U5      nUUU4$ )Nr   r#   )NN)r   r   r   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.        )r   r   r9   )r   r   r   r   r   r   r   r   r   r   updater   r   r!   _attn_implementationgetr   r   r   r   r   r   r   )r1   r6   r   r   r   r   r   r   r   r   q_lenr   query_statesr   r   r   r   cache_kwargsattention_interfacer   r   s                        r3   r>   !GraniteMoeSharedAttention.forwardf  s    &**,A{{=1[[/
{{=1#((T^^T]]S]]^_abc__S1I1I4==Yccdeghi
#((T5M5Mt}}]gghiklm*=*I&|S*';LVY[^'_$L%#&sUL'5'<'<ZW[WeWegs't$J(?;;++w6{{//69fjjI\^c>d>d##L
 '>dkk>^>^&_#$7	%
  $}}C$2H2HLL	%
 	%
!\ "&&s26kk+.L.88r5   )r   r!   r   r(   r   r   r   r   r   r   r   r   r   r   N)NNNFNN)r@   rA   rB   rC   rD   r   r   r|   r'   rE   rF   
LongTensorr   boolr   r>   rG   rH   rI   s   @r3   r   r   C  s    G`5 `(3- ` `F 2637*.59KO69||69 !.69 u//0	69
 !69 69 !!1!1269 &eELL%,,,F&GH69 
u||Xell3XeELL>Q5RR	S69 69r5   r   c                     ^  \ rS rSrS\S\4U 4S jjr        SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\   S\	\R                     S\	\   S\	\\R                  \R                  4      S\\R                  \	\\R                  \R                  4      4   4S jjrSrU =r$ )GraniteMoeSharedDecoderLayeri  r!   r   c                 ~  > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        UR                  U l        UR                  S:X  a  S U l        g [        U5      U l        g )N)r!   r   rR   r   )r&   r'   r(   r   	self_attnr   block_sparse_moerK   rms_norm_epsinput_layernormpost_attention_layernormresidual_multiplierr*   r   
shared_mlpr   s      r3   r'   %GraniteMoeSharedDecoderLayer.__init__  s    !--2&V 3F ;6v7I7IvObObc(?@R@RX^XkXk(l%#)#=#= "("A"AQ"F$L_`fLgr5   r6   r   r   r   r   r   r   output_router_logitsr   r7   c
                    UnU R                  U5      nU R                  " SUUUUUUUU	S.U
D6u  pnXU R                  -  -   nUnU R                  U5      nU R	                  U5      u  pU R
                  c  UnOXR                  U5      -   nXU R                  -  -   nU4nU(       a  UU4-  nU(       a  UU4-  nU(       a  UU4-  nU$ )aY  
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.
    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`).
    past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
    cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
        Indices depicting the position of the input sequence tokens in the sequence
    output_router_logits (`bool`, *optional*):
        Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
        should not be returned during inference.
    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.
    kwargs (`dict`, *optional*):
        Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
        into the model
)r6   r   r   r   r   r   r   r    )r  r  r  r  r  r  )r1   r6   r   r   r   r   r   r   r  r   r   residualself_attn_weightspresent_key_valuemoe_hidden_statesr   outputss                    r3   r>   $GraniteMoeSharedDecoderLayer.forward  s
   L !,,]; ?Cnn 
?
')%)/) 3
?
 
?
;*; !43K3K#KK !55mD+/+@+@+O(??"-M-0NNM 43K3K#KK ")++G)++G''Gr5   )r  r(   r  r  r  r  r  )NNNFFNFN)r@   rA   rB   rC   r   r|   r'   rE   rF   r   r  r   r  r   FloatTensorr>   rG   rH   rI   s   @r3   r  r    s!   
h5 
h# 
h 2637*.,1$)59/4KOP||P !.P u//0	P
 !P $D>P D>P !!1!12P 'tnP &eELL%,,,F&GHP 
u  (51B1BEDUDU1U+V"WW	XP Pr5   r  c                   F    \ rS rSr\rSrSrS/rS/r	Sr
SrSrSrSrS rSrg	)
GraniteMoeSharedPreTrainedModeli  modelTr  past_key_valuesFc                 X   [        U[        R                  5      (       ak  UR                  R                  R                  SU R                  R                  S9  UR                  b%  UR                  R                  R                  5         g g [        U[        R                  5      (       ax  UR                  R                  R                  SU R                  R                  S9  UR                  b2  UR                  R                  UR                     R                  5         g g [        U[        5      (       a&  UR                  R                  R                  S5        g [        U[        5      (       a9  UR                  R                  R                  SU R                  R                  S9  g g )Nr  )rZ   stdg      ?)
isinstancer   r-   rP   datanormal_r!   initializer_ranger%   zero_	Embeddingpadding_idxrK   fill_rf   )r1   r   s     r3   _init_weights-GraniteMoeSharedPreTrainedModel._init_weights  s*   fbii((MM&&CT[[5R5R&S{{&  &&( '--MM&&CT[[5R5R&S!!-""6#5#56<<> . 788MM$$S) ?@@MM&&CT[[5R5R&S Ar5   r  N)r@   rA   rB   rC   r   config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_cache_class_supports_quantized_cache_supports_static_cacher1  rG   r  r5   r3   r$  r$    sH    )L&*#78#4"5!N  $"Tr5   r$  c                   l   ^  \ rS rSrSS\4U 4S jjjr\R                  " 5       \S 5       5       r	Sr
U =r$ )GraniteMoeSharedRotaryEmbeddingi  r!   c                   > [         TU ]  5         [        US5      (       aH  UR                  b;  UR                  R	                  SUR                  R	                  S5      5      U l        OSU l        UR                  U l        UR                  U l        Xl	        [        U R
                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                  U l        g )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r&   r'   hasattrr@  r  rA  max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr!   r   rope_init_fnattention_scalingregister_bufferrD  original_inv_freq)r1   r!   r   rD  r2   s       r3   r'   (GraniteMoeSharedRotaryEmbedding.__init__  s    6>**v/B/B/N#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r5   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r9   r   mpscpuF)device_typeenabledr#   r:   )rV   )rD  r   r   ra   rW   r   r)  rB  strrE   autocastr   rs   r   rK  r   rV   )
r1   r   r   inv_freq_expandedposition_ids_expandedrR  freqsembr   r   s
             r3   r>   'GraniteMoeSharedRotaryEmbedding.forward-  sR    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E'E!((--[`J`ahhmmfk^^UC&,,.1F1L1L1NNYYZ[]^_E))UN3C'')d444C'')d444C	 D vvAGGv$cff177f&;;; DCs   $BF  
F.)rK  r!   rH  rM  rI  rJ  rA  r
  )r@   rA   rB   rC   r   r'   rE   no_gradr   r>   rG   rH   rI   s   @r3   r>  r>    s7    /5 / /" ]]_<  <r5   r>  c                   @  ^  \ rS rSrS\4U 4S jjrS rS r\           SS\	\
R                     S\	\
R                     S\	\
R                     S	\	\\\\
R                      4      S
\	\
R                      S\	\   S\	\   S\	\   S\	\   S\	\   S\	\
R                     S\\\4   4S jj5       r SS\\
R                  S4   S\
R                  S\
R                  S	\S\4
S jjr\S\
R                  S\S\S\
R0                  S\
R                  S\4S j5       rSrU =r$ )GraniteMoeSharedModeli=  r!   c           	      8  > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  UR                  S9U l        SU l        UR$                  U l        UR                  U l        UR&                  U l        U R                  U R(                  -  U l        UR,                  U l        UR.                  U l        UR0                  U l        U R0                  S:X  a  [3        U5      OS U l        U R7                  5         g s  snf )Nr  Frope)r&   r'   pad_token_idr/  
vocab_sizer   r.  r(   embed_tokens
ModuleListro   num_hidden_layersr  layersrK   r  normgradient_checkpointingembedding_multiplierr   r   r   rG  
rope_thetaposition_embedding_typer>  
rotary_emb	post_initr   s      r3   r'   GraniteMoeSharedModel.__init__?  sE    !.. ++LL):):F<N<NPTP`P`ammNSTZTlTlNmnNm)&<Nmn
 ,F,>,>FDWDWX	&+#$*$?$?!!--33((DNN:'-'E'E$ ++'-'E'E$EIEaEaekEk9&Aqu 	! os   Fc                     U R                   $ r
  rb  rb   s    r3   get_input_embeddings*GraniteMoeSharedModel.get_input_embeddingsX  s       r5   c                     Xl         g r
  ro  r1   r   s     r3   set_input_embeddings*GraniteMoeSharedModel.set_input_embeddings[  s    !r5   	input_idsr   r   r&  inputs_embedsr   r   output_hidden_statesr  return_dictr   r7   c                 t   Ub  UOU R                   R                  nUb  UOU R                   R                  nUb  UOU R                   R                  nU
b  U
OU R                   R                  n
US L US L-  (       a  [        S5      eU R                  (       a/  U R                  (       a  U(       a  [        R                  S5        SnUc  U R                  U5      nXPR                  -  nSnU(       aB  [        U[        5      (       d-  Sn[        R                  " U5      n[        R                  S5        UcD  Ub  UR!                  5       OSn["        R$                  " XUR&                  S   -   UR(                  S9nUc  UR+                  S5      nU R-                  X%XU5      nUnS nU R.                  b  U R/                  X5      nU(       a  S	OS nU(       a  S	OS nU	(       a  S	OS nS nU R0                   HZ  nU(       a  UU4-  nU" UUUUUUUU	US
9	nUS   nU(       a  UU(       a  SOS   nU(       a	  UUS   4-  nU	(       d  MQ  UUS   4-  nM\     U R3                  U5      nU(       a  UU4-  nU(       a  UOS nU(       a  UR5                  5       nU
(       d  [7        S UUUU4 5       5      $ [9        UUUUUS9$ )Nz:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.FTzWe detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)r   r   r   r  )r   r   r   r   r   r   r  r   r#   r9   c              3   .   #    U  H  oc  M  Uv   M     g 7fr
  r  ).0vs     r3   	<genexpr>0GraniteMoeSharedModel.forward.<locals>.<genexpr>  s     t$bq$bs   	)last_hidden_stater&  r6   
attentionsr   )r!   r   rx  r   use_return_dictr   rg  r   r   r   rb  rh  r)  r   r   from_legacy_cacheget_seq_lengthrE   arangera   r   r   _update_causal_maskrk  re  rf  to_legacy_cacher`   r   )r1   rv  r   r   r&  rw  r   r   rx  r  ry  r   return_legacy_cachepast_seen_tokensr   r6   r   all_hidden_statesall_self_attnsall_router_logitsnext_decoder_cachedecoder_layerlayer_outputs
next_caches                           r3   r>   GraniteMoeSharedModel.forward^  s    2C1N-TXT_T_TqTq$8$D $++JjJj 	 "+!6IDKK<Q<Q	%0%<k$++B]B]-t";<YZZ&&4==Yj I  --i8M%(A(AA#Z??"&*<<_MO]
 !CRC^==?de"\\ ]5H5H5K"KTaThThN )33A6L..>L]

 &"??&"&//-"N #7BD0d"6BD!![[M#!m%55!)*)."3#-%9$7
M *!,M%28I1q%Q" =#3"55##!mB&7%99!3 )6 		-0  -!11+4'$
#335Jt]J@QSa$bttt%+&+%+
 	
r5   r   input_tensorc           	         U R                   R                  S:X  a  Ub  US:H  R                  5       (       a  U$ g U R                   R                  S:X  a,  [        U[        R
                  5      (       a  [        U5      nU$ Ub  UR                  5       OSnUb  UR                  OSnU R                   R                  S:X  a5  U(       d.  U(       d'  [        R                  " UUUU R                  S9(       a  g UR                  nUR                  S   n	U(       a  UR                  5       n
O5[        U[        R
                  5      (       a  UR                  S	   OXi-   S-   n
U R                  UU	U
UUUR                  S   S
9nU R                   R                  S:X  aZ  UbW  UR                   R"                  S;   a=  U(       d6  [        R$                  " U5      R&                  n[        R(                  " X5      nU$ )Nflash_attention_2r  flex_attentionr   Fr   )rw  past_key_values_lengthis_trainingr   r9   )sequence_lengthtarget_lengthrV   r   
batch_size)cudaxpunpu)r!   r  anyr)  rE   rF   r   r  is_compileabler   _ignore_causal_mask_sdpar   rV   ra   get_max_cache_shape5_prepare_4d_causal_attention_mask_with_cache_positionr   rB  finfomin_unmask_unattended)r1   r   r  r   r&  r   r  using_compilable_cacherV   r  r  r   	min_dtypes                r3   r  )GraniteMoeSharedModel._update_causal_mask  s    ;;++/BB)~/D.I.I.K.K%%;;++/??.%,,77!<^!L!!
 @O?Z?99;`aCRC^!?!?di ;;++v5>T]n%>>*'7 MM	 ""&,,Q/!+??AM nell;; $$R(%7!;  PP+')#))!, Q 
 KK,,6*%%**.DD%
 E*..I0CCK[Kr5   r  r  rV   r  c                    U b  U R                  5       S:X  a  U nU$ [        R                  " U5      R                  n[        R                  " X4XUR
                  S9nUS:w  a  [        R                  " USS9nU[        R                  " X$R
                  S9UR                  SS5      :  -  nUSSSS2SS24   R                  USSS5      nU b  UR                  5       nU R                  S   n	USS2SS2SS2SU	24   U SS2SSSS24   R                  UR
                  5      -   n
U
S:H  n
USS2SS2SS2SU	24   R                  X5      USS2SS2SS2SU	24'   U$ )	a  
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

Args:
    attention_mask (`torch.Tensor`):
        A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
        `(batch_size, 1, query_length, key_value_length)`.
    sequence_length (`int`):
        The sequence length being processed.
    target_length (`int`):
        The target length: when generating with static cache, the mask should be as long as the static cache,
        to account for the 0 padding, the part of the cache that is not filled yet.
    dtype (`torch.dtype`):
        The dtype to use for the 4D attention mask.
    cache_position (`torch.Tensor`):
        Indices depicting the position of the input sequence tokens in the sequence.
    batch_size (`torch.Tensor`):
        Batch size.
N   )
fill_valuerV   r   r   )diagonalr{  r9   r   )r;   rE   r  r  fullr   triur  r   r   clonera   rW   masked_fill)r   r  r  rV   r   r  r   r   r  mask_lengthpadding_masks              r3   r  KGraniteMoeSharedModel._prepare_4d_causal_attention_mask_with_cache_position  s}   < %.*<*<*>!*C(K* ' E*..I** 0Y\j\q\qK !##jjqA5<<>S>STWeWmWmnprsWtttK%dD!Q&67>>z1bRTUK))//1,2226*1aL[L+@ANSTVZ\`bcScDdDgDg&&E    ,q05@Aq,;,AV5W5c5c 6Aq!\k\12 r5   )rb  rh  rg  r   r(   re  rG  rf  r   r/  rj  ri  rk  ra  )NNNNNNNNNNN)F)r@   rA   rB   rC   r   r'   rp  rt  r   r   rE   r  rF   r   r   r   r"  r  r   r   r>   r  staticmethodr|   rV   r  rG   rH   rI   s   @r3   r]  r]  =  s   5 2!"  151537KO59$(,0/3/3&*59s
E,,-s
 !.s
 u//0	s

 "%tE4E4E/F(F"GHs
   1 12s
 D>s
 $D>s
 'tns
 'tns
 d^s
 !!1!12s
 
u--	.s
 s
v #(BellK78B llB 	B
 B  BH 444 4 {{	4
 4 4 4r5   r]  gate_logitsrh   c                    U b  [        U [        5      (       d  g[        U [        5      (       aC  U S   R                  n[        R                  " U  Vs/ s H  oUR                  U5      PM     snSS9n[        R                  R                  R                  WSS9n[        R                  " XrSS9u  p[        R                  R                  R                  X5      n
Uc:  [        R                  " U
R                  5       SS9n[        R                  " USS9nGOUR                  u  pUR                  S   X-  -  nUSSS2SS2SS4   R                  XXU45      R                  SX!5      R                  W5      n[        R                   " U
R                  5       U-  SS9[        R                   " USS9-  nUSSS2SS2S4   R                  XX45      R                  SU5      R                  U5      n[        R                   " UU-  SS9[        R                   " USS9-  n[        R                   " XR#                  S5      -  5      nUU-  $ s  snf )ap  
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.

Args:
    gate_logits:
        Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
        shape [batch_size X sequence_length, num_experts].
    num_experts:
        Number of experts
    top_k:
        The number of experts to route per-token, can be also interpreted as the `top-k` routing
        parameter.
    attention_mask (`torch.Tensor`, *optional*):
        The attention_mask used in forward function
        shape [batch_size X sequence_length] if not None.

Returns:
    The auxiliary loss.
Nr   r:   r9   )r)  r`   r   rE   rs   rW   r   r   r   r   one_hotrZ   r   ra   r   r   r   r   )r  rh   r   r   compute_device
layer_gateconcatenated_gate_logitsrouting_weightsr   selected_expertsexpert_masktokens_per_expertrouter_prob_per_expertr  r  rd  expert_attention_mask router_per_expert_attention_maskoverall_losss                      r3   load_balancing_loss_funcr  P  s+   : *[%"@"@+u%%$Q..#(99^i-j^iPZmmN.K^i-jpq#r hh))112JPR1SO**_DA((%%--.>LK!JJ{'8'8':B "'O!C&4&:&:#
4::1=*B^_ 4AtT12V&OKXYWR,R	 	 "IIk&7&7&9<Q&QWXY\a\e\e!q]
 
 4At+,V&OQRWR%R	 	) "'?=]+]cd!ehmhqhq,!i
 "
 99.1Q1QRS1TTUL+%%[ .ks   Ic                      ^  \ rS rSrS/rS\4U 4S jjrS rS rS r	S r
S	 rS
 r\             SS\\R                      S\\R"                     S\\R                      S\\\\\R*                     4      S\\R*                     S\\R                      S\\   S\\   S\\   S\\   S\\   S\\R                      S\\\R"                  4   S\\\4   4S jj5       r\S 5       rSrU =r$ )GraniteMoeSharedForCausalLMi  zlm_head.weightr!   c                 J  > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        UR                  U l	        UR                  U l        UR                  U l        U R                  5         g )NFr$   )r&   r'   r]  r%  ra  r   r-   r(   lm_headrouter_aux_loss_coefr   rh   r   rl  r0   s     r3   r'   $GraniteMoeSharedForCausalLM.__init__  s     *62
 ++yy!3!3V5F5FUS$*$?$?!!33#)#=#=  	r5   c                 .    U R                   R                  $ r
  r%  rb  rb   s    r3   rp  0GraniteMoeSharedForCausalLM.get_input_embeddings  s    zz&&&r5   c                 $    XR                   l        g r
  r  rs  s     r3   rt  0GraniteMoeSharedForCausalLM.set_input_embeddings  s    "'

r5   c                     U R                   $ r
  r  rb   s    r3   get_output_embeddings1GraniteMoeSharedForCausalLM.get_output_embeddings  s    ||r5   c                     Xl         g r
  r  )r1   new_embeddingss     r3   set_output_embeddings1GraniteMoeSharedForCausalLM.set_output_embeddings  s    %r5   c                     Xl         g r
  r%  )r1   decoders     r3   set_decoder'GraniteMoeSharedForCausalLM.set_decoder  s    
r5   c                     U R                   $ r
  r  rb   s    r3   get_decoder'GraniteMoeSharedForCausalLM.get_decoder  s    zzr5   rv  r   r   r&  rw  labelsr   r   rx  r  ry  r   logits_to_keepr7   c                    Ub  UOU R                   R                  nU
b  U
OU R                   R                  n
U	b  U	OU R                   R                  n	Ub  UOU R                   R                  nU R                  UUUUUUUU	U
UUS9nUS   n[        U[        5      (       a  [        U* S5      OUnU R                  USS2USS24   5      nUU R                   R                  -  nSnUb:  UR                  5       nU R                  " UU4SU R                   R                  0UD6nSnU
(       af  [        U(       a  UR                  OUS   U R                   U R"                  U5      nUb+  UU R$                  UR'                  UR(                  5      -  -  nU(       d!  U4USS -   nU
(       a  U4U-   nUb  U4U-   $ U$ [+        UUUUR,                  UR.                  UR0                  UR                  S9$ )a  
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
    config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
    (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

Example:

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

>>> model = GraniteMoeSharedForCausalLM.from_pretrained("ibm/PowerMoE-3b")
>>> tokenizer = AutoTokenizer.from_pretrained("ibm/PowerMoE-3b")

>>> 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."
```N)rv  r   r   r&  rw  r   r   rx  r  ry  r   r   ra  r9   r   )lossaux_lossr   r&  r6   r  r   )r!   r   r  rx  r  r%  r)  r|   slicer  logits_scalingr   loss_functionra  r  r   rh   r   r  rW   r   r   r&  r6   r  )r1   rv  r   r   r&  rw  r  r   r   rx  r  ry  r   r  r   r   r6   slice_indicesr   r  r  outputs                         r3   r>   #GraniteMoeSharedForCausalLM.forward  s   P 2C1N-TXT_T_TqTq$8$D $++JjJj 	 %9$D $++JjJj 	 &1%<k$++B]B] **)%+'/!5!5#)  
  
8B>SV8W8W~ot4]kmA}a,?@A$++444\\^F%%  ;;11 	D /)4%%'"+  ((	H !11HKK4LLLY,F#"v-'+'7D7V#CVC(#33!//))!//
 	
r5   c                 P   ^ SnU  H  nU[        U4S jU 5       5      4-  nM     U$ )Nr  c              3   x   >#    U  H/  oR                  S TR                  UR                  5      5      v   M1     g7f)r   N)index_selectrW   r   )r}  
past_statebeam_idxs     r3   r  =GraniteMoeSharedForCausalLM._reorder_cache.<locals>.<genexpr>6  s1     ncmU_--aZ=N=N1OPPcms   7:)r`   )r&  r  reordered_past
layer_pasts    `  r3   _reorder_cache*GraniteMoeSharedForCausalLM._reorder_cache1  s8    )Jncmnn N * r5   )r  r%  rh   r   r  ra  )NNNNNNNNNNNNr   )r@   rA   rB   rC   _tied_weights_keysr   r'   rp  rt  r  r  r  r  r   r   rE   r  rF   r   r   r   r"  r  r|   r   r   r>   r  r  rG   rH   rI   s   @r3   r  r    s   *+5 '(&  151537KO59-1$(,0/3/3&*5934j
E,,-j
 !.j
 u//0	j

 "%tE4E4E/F(F"GHj
   1 12j
 ))*j
 D>j
 $D>j
 'tnj
 'tnj
 d^j
 !!1!12j
 c5<</0j
  
u//	0!j
 j
X  r5   r  )r  r]  r$  )Nr   )r  )Nr#   N)Btypingr   r   r   r   r   rE   torch.nn.functionalr   r   rq   activationsr
   cache_utilsr   r   
generationr   modeling_attn_mask_utilsr   modeling_layersr   modeling_outputsr   r   r   modeling_rope_utilsr   r   modeling_utilsr   r   utilsr   r   r   configuration_granitemoesharedr   !torch.nn.attention.flex_attentionr   integrations.flex_attentionr   
get_loggerr@   r   Moduler   rK   rf   r~   r   r   r   rF   r|   r   r   r   r   r  r$  r>  r]  r  r  __all__r  r5   r3   <module>r     sF  , : 9     ! . ) > 9 j j K F J J B  !!;J 
		H	%")) 4Jbii J(*bii *Z-S -S`9+")) 9+x(6	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % %:Y9		 Y9x]#= ]@ To T T6<bii <D O; O Oh "&
-1	O&u||U5<<%8$>?O&#O& U\\*	O&
 5<<O&dV"A? Vr fr5   