
    Z j)S                     N   S SK Jr  S SKJr  S SKrS SKJr  S SKJs  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  SSKJrJr  SSKJrJr  SSKJr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+  SSK,J-r-   " S S\R\                  5      r/ " S S\R\                  5      r0 " S S\R\                  5      r1S r2S\Rf                  S\4S\Rf                  4S jr5 S6S\R\                  S \Rf                  S!\Rf                  S"\Rf                  S#\Rf                  S-  S$\6S%\6S&\"\$   4S' jjr7S7S( jr8\" \85       " S) S*\R\                  5      5       r9 " S+ S,\5      r:\% " S- S.\ 5      5       r;\% " S/ S0\;5      5       r<\% " S1 S2\;\5      5       r= " S3 S4\\;5      r>/ S5Qr?g)8    )Callable)OptionalN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernelized_func)create_causal_mask) GenericForSequenceClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )
OlmoConfigc                   r   ^  \ rS rSrSrS\SS4U 4S jjrS\R                  S\R                  4S jr	S	r
U =r$ )
OlmoLayerNorm1   z/LayerNorm but with no learnable weight or bias.hidden_sizereturnNc                 2   > [         TU ]  5         U4U l        g N)super__init__normalized_shape)selfr    	__class__s     w/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/olmo/modeling_olmo.pyr%   OlmoLayerNorm.__init__4   s    !,    hidden_statesc                     UR                   n[        R                  " UR                  [        R
                  S9U R                  S S SS9R                  U5      $ )Ndtypegh㈵>)eps)r/   F
layer_normtotorchfloat32r&   )r'   r,   
orig_dtypes      r)   forwardOlmoLayerNorm.forward8   sO    "((
||M,,5==,A4CXCXZ^`djnorr
 	
r+   )r&   )__name__
__module____qualname____firstlineno____doc__intr%   r4   Tensorr7   __static_attributes____classcell__r(   s   @r)   r   r   1   s9    9/C /D /
U\\ 
ell 
 
r+   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )OlmoMLP?   c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l        [
        R                  " U R                  U R                  SS9U l	        [        UR                     U l        g NFbias)r$   r%   configr    intermediate_sizennLinear	gate_projup_proj	down_projr   
hidden_actact_fnr'   rJ   r(   s     r)   r%   OlmoMLP.__init__@   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r+   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ r#   )rP   rR   rN   rO   )r'   xrP   s      r)   r7   OlmoMLP.forwardJ   s6    NN4;;t~~a/@#ADLLQRO#ST	r+   )rR   rJ   rP   rN   r    rK   rO   )r9   r:   r;   r<   r%   r7   r@   rA   rB   s   @r)   rD   rD   ?   s    0 r+   rD   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )OlmoRotaryEmbeddingO   inv_freqNrJ   c                   > [         TU ]  5         UR                  U l        UR                  U l        Xl        U R
                  R                  S   U l        U R                  nU R                  S:w  a  [        U R                     nU" U R
                  U5      u  o@l
        U R                  SUSS9  U R                  SUR                  5       SS9  g )N	rope_typedefaultr[   F)
persistentoriginal_inv_freq)r$   r%   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrJ   rope_parametersr]   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r'   rJ   devicerope_init_fnr[   r(   s        r)   r%   OlmoRotaryEmbedding.__init__R   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr+   ri   ztorch.deviceseq_lenr!   ztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	aH  
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).

rope_thetahead_dimNg      ?r      r.   )ri   r/   )	rd   getattrr    num_attention_headsr4   arangeint64r3   float)rJ   ri   rl   basedimattention_factorr[   s          r)   re   3OlmoRotaryEmbedding.compute_default_rope_parametersb   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r+   c                    U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        X4$ ! , (       d  f       WW	4$ = f)
Nr   r   mpscpuF)device_typeenabledrp   rw   )r[   ru   expandshaper3   ri   
isinstancetypestrr   	transposer4   catcosrf   sin)
r'   rV   position_idsinv_freq_expandedposition_ids_expandedr~   freqsembr   r   s
             r)   r7   OlmoRotaryEmbedding.forward   s7    !MM$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
 x DC
 Cxs   BE&&
E7)rf   rJ   rb   rc   r]   r#   )NNN)r9   r:   r;   r<   r4   r?   __annotations__r   r%   staticmethodr   r>   tupleru   re   no_gradr   r7   r@   rA   rB   s   @r)   rY   rY   O   s    llVz V V  $(+/"*T!*(* t* 
~u$	%	* *: ]]_
  
r+   rY   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$ )z*Rotates half the hidden dims of the input..Nr{   rp   r   )r   r4   r   )rV   x1x2s      r)   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r+   r,   n_repr!   c                     U R                   u  p#pEUS:X  a  U $ 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      $ )z
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
r   N)r   r   reshape)r,   r   batchnum_key_value_headsslenro   s         r)   	repeat_kvr      s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr+   modulequerykeyvalueattention_maskscalingdropoutkwargsc                    [        X R                  5      n[        X0R                  5      n	[        R                  " XR	                  SS5      5      U-  n
Ub  X-   n
[
        R                  R                  U
S[        R                  S9R                  UR                  5      n
[
        R                  R                  XU R                  S9n
[        R                  " X5      nUR	                  SS5      R                  5       nX4$ )Nrp   r   r{   )rw   r/   )ptrainingr   )r   num_key_value_groupsr4   matmulr   rL   
functionalsoftmaxr5   r3   r/   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r)   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r+   c                    U R                   UR                   peUR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nUR                  U5      UR                  U5      4$ )aI  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.
)r/   	unsqueezer   r3   )	qkr   r   unsqueeze_dimq_typek_typeq_embedk_embeds	            r)   apply_rotary_pos_embr      sv    $ WWaggF
--
&C
--
&Cw;q>C/0Gw;q>C/0G::fwzz&111r+   c                      ^  \ rS rSrSrS\S\4U 4S jjr SS\R                  S\
\R                  \R                  4   S	\R                  S-  S
\S-  S\
\R                  \R                  S-  4   4
S jjrSrU =r$ )OlmoAttention   z=Multi-headed attention from 'Attention Is All You Need' paperrJ   	layer_idxc                 P  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        S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                  -  UR                  S9U l        [        R                  " UR                  U R                  -  UR
                  UR                  S9U l        g )Nro   g      TrH   )r$   r%   rJ   r   rq   r    rr   ro   r   r   r   attention_dropout	is_causalrL   rM   attention_biasq_projk_projv_projo_projr'   rJ   r   r(   s      r)   r%   OlmoAttention.__init__   sI   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r+   Nr,   position_embeddingsr   past_key_valuesr!   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      nU R                  U5      n	U R	                  U5      n
U R
                  R                  b  UR                  U R
                  R                  * U R
                  R                  S9  U	R                  U R
                  R                  * U R
                  R                  S9  U
R                  U R
                  R                  * U R
                  R                  S9  UR                  U5      R                  SS5      nU	R                  U5      R                  SS5      n	U
R                  U5      R                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R
                  R                  [         5      nU" U UU	U
U4U R"                  (       d  SOU R$                  U R&                  S.UD6u  pUR(                  " / UQSP76 R+                  5       nU R-                  U5      nX4$ )Nr{   )minmaxr   rp           )r   r   )r   ro   r   r   r   rJ   clip_qkvclamp_viewr   r   updater   r   get_interface_attn_implementationr   r   r   r   r   r   r   )r'   r,   r   r   r   r   input_shapehidden_shapequery_statesr   r   r   r   attention_interfacer   r   s                   r)   r7   OlmoAttention.forward   s    $))#2.88b8$--8{{=1[[/
{{=1;;+T[[%9%9$9t{{?S?ST4;;#7#7"7T[[=Q=QRT[[%9%9$9t{{?S?ST#((6@@AF__\2<<QB
#((6@@AF&#7RU#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
! "));;;;FFHkk+.((r+   )r   rJ   ro   r   r   r   r   r   r   r   r   r#   )r9   r:   r;   r<   r=   r   r>   r%   r4   r?   r   r   r7   r@   rA   rB   s   @r)   r   r      s    G
z 
c 
8 )-/)||/) #5<<#=>/) t+	/)
 /) 
u||U\\D00	1/) /)r+   r   c                     ^  \ rS rSrS\S\4U 4S jjr     SS\R                  S\R                  S-  S\R                  S-  S	\
S-  S
\S-  S\\R                  \R                  4   S-  S\\   S\R                  4S jjrSrU =r$ )OlmoDecoderLayeri"  rJ   r   c                    > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  5      U l        [        UR                  5      U l	        g )N)rJ   r   )
r$   r%   r    r   	self_attnrD   mlpr   input_layernormpost_attention_layernormr   s      r)   r%   OlmoDecoderLayer.__init__#  sY    !--&fJ6?,V-?-?@(5f6H6H(I%r+   Nr,   r   r   r   	use_cacher   r   r!   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r,   r   r   r   r   r    )r   r   r   r   )
r'   r,   r   r   r   r   r   r   residual_s
             r)   r7   OlmoDecoderLayer.forward,  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r+   )r    r   r   r   r   )NNNFN)r9   r:   r;   r<   r   r>   r%   r4   r?   
LongTensorr   boolr   r   r   r7   r@   rA   rB   s   @r)   r   r   "  s    Jz Jc J /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r+   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
OlmoPreTrainedModeliL  rJ   modelTr   r   )r,   
attentionsr   N)r9   r:   r;   r<   r   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr@   r   r+   r)   r   r   L  sQ    &*#+,#4"5N!"&)#r+   r   c                     ^  \ rS rSrS\4U 4S jjr\\\      SS\	R                  S-  S\	R                  S-  S\	R                  S-  S\S-  S	\	R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )	OlmoModeli_  rJ   c           	        > [         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                  5      U l        [!        US9U l        SU l        U R'                  5         g s  snf )NrJ   F)r$   r%   pad_token_idpadding_idx
vocab_sizerL   	Embeddingr    embed_tokens
ModuleListrangenum_hidden_layersr   layersr   normrY   
rotary_embgradient_checkpointing	post_initr   s      r)   r%   OlmoModel.__init__a  s     !.. ++LL):):F<N<NPTP`P`ammBGH`H`BabBaYf0Bab
 "&"4"45	-V<&+# 	 cs   C6N	input_idsr   r   r   inputs_embedsr   r   r!   c           
      >   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  S U R                  R                    H  nU" U
4U	UUUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r   )ri   )rJ   r  r   r   r   )r   )r   r   r   r   r   )last_hidden_stater   )
ValueErrorr  r   rJ   get_seq_lengthr4   rs   r   ri   r   r   r	  r  r  r  r   )r'   r  r   r   r   r  r   r   past_seen_tokenscausal_maskr,   r   decoder_layers                r)   r7   OlmoModel.forwardq  sF    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW![[)H4;;+H+HIM)*$7) /# M J 		-0&++
 	
r+   )r  r
  r  r  r   r	  r  )NNNNNN)r9   r:   r;   r<   r   r%   r   r   r   r4   r   r?   r   FloatTensorr   r   r   r   r7   r@   rA   rB   s   @r)   r   r   _  s    z     .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r+   r   c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )OlmoForCausalLMi  zlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr,   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g rG   )
r$   r%   r   r   r  rL   rM   r    r  r  rS   s     r)   r%   OlmoForCausalLM.__init__  sU     v&
 ++yy!3!3V5F5FUS 	r+   Nr  r   r   r   r  labelsr   logits_to_keepr   r!   c	           
      |   U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U	D6n[        UUU
R                  U
R                  U
R                  S9$ )ai  
Example:

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

>>> model = OlmoForCausalLM.from_pretrained("meta-olmo/Olmo-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo/Olmo-2-7b-hf")

>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")

>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```)r  r   r   r   r  r   N)r  r  r  )lossr  r   r,   r   r   )r   r  r   r>   slicer  loss_functionrJ   r  r   r   r,   r   )r'   r  r   r   r   r  r  r   r   r   outputsr,   slice_indicesr  r"  s                  r)   r7   OlmoForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r+   )r  r   r  )NNNNNNNr   )r9   r:   r;   r<   _tied_weights_keys_tp_plan_pp_planr%   r   r   r4   r   r?   r   r  r   r>   r   r   r   r7   r@   rA   rB   s   @r)   r  r    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r+   r  c                       \ rS rSrSrg)OlmoForSequenceClassificationi  r   N)r9   r:   r;   r<   r@   r   r+   r)   r,  r,    s    r+   r,  )r  r,  r   r   )r   )r   )@collections.abcr   typingr   r4   torch.nnrL   torch.nn.functionalr   r1   activationsr   cache_utilsr   r   
generationr	   integrationsr
   masking_utilsr   modeling_layersr   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_olmor   Moduler   rD   rY   r   r?   r>   r   ru   r   r   r   r   r   r   r  r,  __all__r   r+   r)   <module>rA     s  4 %      ! . ) / / [ O K F & I I G 5 *
BII 
bii  =")) =@(	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%224 )*I)BII I) +I)X'1 'T /  $ F
# F
 F
R F
)? F
 F
R	$DFY 	 cr+   