
    Z jT                     b   S SK r S SKJr  S SKJr  S SK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  SS	KJr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#  SSK$J%r%J&r&  SSK'J(r(J)r)J*r*  SSK+J,r,  SSK-J.r.   " S S\R                  R^                  5      r0 " S S\R^                  5      r1\" S5      S6S j5       r2S\Rf                  S\4S\Rf                  4S jr5 S7S\R^                  S \Rf                  S!\Rf                  S"\Rf                  S#\Rf                  S-  S$\6S%\6S&\#\%   4S' jjr7S( r8\" \25       " S) S*\R^                  5      5       r9 " S+ S,\R^                  5      r: " S- S.\5      r;\& " S/ S0\!5      5       r<\& " S1 S2\<5      5       r=\& " S3 S4\<\5      5       r>/ S5Qr?g)8    N)Callable)Optional   )initialization)ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_func_from_hubuse_kernelized_func)create_causal_mask)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   )NanoChatConfigc                   F   ^  \ rS rSrSS\4U 4S jjjrS rS rS rSr	U =r
$ )	NanoChatRMSNorm-   epsc                 .   > [         TU ]  5         Xl        g N)super__init__r!   )selfr!   	__class__s     /root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/nanochat/modeling_nanochat.pyr%   NanoChatRMSNorm.__init__.   s        c                     U[         R                  " UR                  S5      R                  SSS9U R                  -   5      -  $ )N   T)keepdim)torchrsqrtpowmeanr!   r&   xs     r(   _normNanoChatRMSNorm._norm2   s4    5;;quuQx}}R}>IJJJr*   c                 ^    U R                  UR                  5       5      R                  U5      $ r#   )r5   floattype_asr3   s     r(   forwardNanoChatRMSNorm.forward5   s"    zz!'')$,,Q//r*   c                      SU R                    3$ )Nzeps=r!   )r&   s    r(   
extra_reprNanoChatRMSNorm.extra_repr8   s    dhhZ  r*   r=   )gư>)__name__
__module____qualname____firstlineno__r8   r%   r5   r:   r>   __static_attributes____classcell__r'   s   @r(   r   r   -   s)    E  K0! !r*   r   c                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )NanoChatRotaryEmbedding<   inv_freqNconfigc                   > [         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defaultrJ   F)
persistentoriginal_inv_freq)r$   r%   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrK   rope_parametersrM   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r&   rK   devicerope_init_fnrJ   r'   s        r(   r%    NanoChatRotaryEmbedding.__init__?   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuUr*   rY   ztorch.deviceseq_lenreturnztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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,   dtype)rY   rb   )	rT   getattrhidden_sizenum_attention_headsr/   arangeint64tor8   )rK   rY   r\   basedimattention_factorrJ   s          r(   rU   7NanoChatRotaryEmbedding.compute_default_rope_parametersO   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 ))r*   c                 L   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r-   r   mpscpuF)device_typeenabledr,   rj   ra   )rJ   r8   expandshaperh   rY   
isinstancetypestrr   	transposer/   catcosrV   sinrb   )
r&   r4   position_idsinv_freq_expandedposition_ids_expandedrp   freqsembrz   r{   s
             r(   r:   NanoChatRotaryEmbedding.forwardm   sN    !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#)rV   rK   rR   rS   rM   r#   NNN)r@   rA   rB   rC   r/   Tensor__annotations__r   r%   staticmethodr   inttupler8   rU   no_gradr   r:   rD   rE   rF   s   @r(   rH   rH   <   s    llV~ V V  (,+/"*%*(* t* 
~u$	%	* *: ]]_<  <r*   rH   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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.
)	unsqueezerotate_half)qkrz   r{   unsqueeze_dimq_embedk_embeds          r(   apply_rotary_pos_embr   }   sS    & --
&C
--
&Cw;q>C/0Gw;q>C/0Gr*   hidden_states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)rt   rs   reshape)r   r   batchnum_key_value_headsslenr`   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$ )Nr,   r   r-   )rj   rb   )ptrainingr   )r   num_key_value_groupsr/   matmulrx   nn
functionalsoftmaxfloat32rh   rb   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r(   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r*   c                     U SSU R                   S   S-  24   nU SU R                   S   S-  S24   n[        R                  " X!* 4SS9$ )zJRotates half the hidden dims of the input with flipped signs for NanoChat..Nr-   r,   rr   )rt   r/   ry   )r4   x1x2s      r(   r   r      sX    	
3"!''"+"""	#B	
3q ""	#B99b#YB''r*   c                     ^  \ rS rSrSrS\S\4U 4S jjr   SS\R                  S\
\R                  \R                  4   S-  S	\R                  S-  S
\S-  S\\   S\
\R                  \R                  S-  4   4S jjrSrU =r$ )NanoChatAttention   z=Multi-headed attention from 'Attention Is All You Need' paperrK   	layer_idxc                   > [         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        [)        UR*                  S9U l        [)        UR*                  S9U l        g )Nr`   g      Tbiasr=   )r$   r%   rK   r   rc   rd   re   r`   r   r   r   attention_dropout	is_causalr   Linearattention_biasq_projk_projv_projo_projr   rms_norm_epsq_normk_normr&   rK   r   r'   s      r(   r%   NanoChatAttention.__init__   so   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 &&*=*=>%&*=*=>r*   Nr   position_embeddingsr   past_key_valuesr   r]   c                 L   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pU R                  U5      nU R                  U	5      n	Ub  UR                  XU R                  5      u  p[        R                  " U R                  R                  [         5      nU" U UU	U
U4U R"                  (       d  SOU R$                  U R&                  S.UD6u  pUR(                  " / UQSP76 R+                  5       nU R-                  U5      nX4$ )Nr-   r   r,           )r   r   )rt   r`   r   viewrx   r   r   r   r   r   updater   r   get_interfacerK   _attn_implementationr   r   r   r   r   r   r   )r&   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rz   r{   attention_interfacer   r   s                   r(   r:   NanoChatAttention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST&#7RU#[  {{<0[[,
&'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8	%
  $}}C$2H2HLL	%
 	%
! "));;;;FFHkk+.((r*   )r   rK   r`   r   r   r   r   r   r   r   r   r   r   r   )r@   rA   rB   rC   __doc__r   r   r%   r/   r   r   r   r   r   r:   rD   rE   rF   s   @r(   r   r      s    G?~ ?# ?: IM.2(,*)||*) #5<<#=>E*) t+	*)
 *) +,*) 
u||U\\D00	1*) *)r*   r   c                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )NanoChatMLPi  c                   > [         TU ]  5         Xl        [        UR                     U l        [        R                  " UR                  UR                  SS9U l
        [        R                  " UR                  UR                  SS9U l        g NFr   )r$   r%   rK   r   
hidden_actactivation_fnr   r   rd   intermediate_sizefc1fc2r&   rK   r'   s     r(   r%   NanoChatMLP.__init__  sh    #F$5$5699V//1I1IPUV99V55v7I7IPUVr*   r   r]   c                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ r#   )r   r   r   )r&   r   s     r(   r:   NanoChatMLP.forward  s4    /**=9/r*   )r   rK   r   r   )
r@   rA   rB   rC   r%   r/   r   r:   rD   rE   rF   s   @r(   r   r     s)    WU\\ ell  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$ )NanoChatDecoderLayeri  rK   r   c                    > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  S9U l	        [        UR                  S9U l
        g )N)rK   r   r=   )r$   r%   rd   r   	self_attnr   mlpr   r   input_layernormpost_attention_layernormr   s      r(   r%   NanoChatDecoderLayer.__init__  sZ    !--*&Nv&.63F3FG(7F<O<O(P%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$ )N)r   r   r|   r   r   r    )r   r   r   r   )
r&   r   r   r|   r   r   r   r   residual_s
             r(   r:   NanoChatDecoderLayer.forward)  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r*   )rd   r   r   r   r   )NNNFN)r@   rA   rB   rC   r   r   r%   r/   r   
LongTensorr   boolr   r   r   r:   rD   rE   rF   s   @r(   r   r     s    	Q~ 	Q# 	Q /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r*   r   c                      ^  \ 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&                  S	S
4U 4S jjrSrU =r$ )NanoChatPreTrainedModeliI  rK   modelTr   r   )r   
attentionsr   r]   Nc           	      (  > [         TU ]  U5        [        U[        5      (       am  [        R
                  " UR                  R                  SU R                  R                  [        R                  " SU R                  R                  -  5      -  S9  g g )Nr   r,   )r2   std)r$   _init_weightsru   r   initnormal_r   weightrK   initializer_rangemathsqrtnum_hidden_layers)r&   r   r'   s     r(   r   %NanoChatPreTrainedModel._init_weights[  si    f%f/00LL$$KK11DIIa$++B_B_>_4`` 1r*   r   )r@   rA   rB   rC   r   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr   Moduler   rD   rE   rF   s   @r(   r   r   I  sn    &*#/0#4"5N!"&-'
BII $  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$ )NanoChatModelie  rK   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [
        R                  " [        UR                  5       Vs/ s H  n[        X5      PM     sn5      U l        [        UR                  S9U l        [#        US9U l        SU l        U R)                  5         g s  snf )Nr=   rK   F)r$   r%   pad_token_idpadding_idx
vocab_sizer   	Embeddingrd   embed_tokens
ModuleListranger  r   layersr   r   normrH   
rotary_embgradient_checkpointing	post_initr   s      r(   r%   NanoChatModel.__init__g  s     !.. ++LL):):F<N<NPTP`P`ammFKFLdLdFefFe!&4Fef
 $(;(;<	1@&+# 	 gs   C4N	input_idsr   r|   r   inputs_embedsr   r   r]   c           	      ^   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc  [        U R                  S9nUcU  Ub  UR	                  5       OSn[
        R                  " UR                  S   UR                  S9U-   nUR                  S5      n[        U R                  UUUUS9n	Un
U R                  XS9nU R                  U
5      n
U R                  S U R                  R                    H  nU" U
4U	UUUS.UD6n
M     U R                  U
5      n
[        U
US	9$ )
Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )rY   )rK   r   r   r   r|   )r|   )r   r   r|   r   )last_hidden_stater   )
ValueErrorr  r	   rK   get_seq_lengthr/   rf   rt   rY   r   r   r  r  r  r  r   )r&   r  r   r|   r   r   r   r   past_seen_tokenscausal_maskr   r   decoder_layers                r(   r:   NanoChatModel.forwardx  sQ    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L(;;')+%
 &"oomoW		-0![[)H4;;+H+HIM)*$7) / M J 		-0&++
 	
r*   )r  r  r  r  r  r  r  )NNNNNN)r@   rA   rB   rC   r   r%   r   r   r   r/   r   r   r   FloatTensorr   r   r   r   r:   rD   rE   rF   s   @r(   r  r  e  s    ~ "   .2.204(,26!%2
##d*2
 t+2
 &&-	2

 2
 ((4/2
 $;2
 +,2
 
!2
    2
r*   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )NanoChatForCausalLMi  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 r   )
r$   r%   r  r   r  r   r   rd   r,  r  r   s     r(   r%   NanoChatForCausalLM.__init__  sU     "6*
 ++yy!3!3V5F5FUS 	r*   Nr  r   r|   r   r   labelsr   logits_to_keepr   r]   c	           
          U R                   " SUUUUUUS.U	D6n
U
R                  n[        U[        5      (       a  [	        U* S5      OUnU R                  USS2USS24   5      nU R                  R                  bF  XR                  R                  -  n[        R                  " U5      nXR                  R                  -  nSnUb  U R                  " XU R                  40 U	D6n[        UUU
R                  U
R                  U
R                  S9$ )a  
Example:

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

>>> model = AutoModelForCausalLM.from_pretrained("karpathy/nanochat-d32")

>>> tokenizer = AutoTokenizer.from_pretrained("karpathy/nanochat-d32")

>>> conversation = [
        {"role": "user", "content": "What is the capital of France?"},
    ]

>>> inputs = tokenizer.apply_chat_template(
        conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
    ).to(device)

>>> with torch.no_grad():
>>>     outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False)

>>> generated_tokens = outputs[0, inputs["input_ids"].shape[1] :]
>>> output = tokenizer.decode(generated_tokens, skip_special_tokens=True)
```)r  r   r|   r   r   r   N)lossr.  r   r   r   r   )r   r"  ru   r   slicer,  rK   final_logit_softcappingr/   tanhloss_functionr  r   r   r   r   )r&   r  r   r|   r   r   r1  r   r2  r   outputsr   slice_indicesr.  r4  s                  r(   r:   NanoChatForCausalLM.forward  s   N ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A;;..:kkAAAFZZ'FkkAAAF%%fdooPPD%#33!//))
 	
r*   )r,  r   r  )NNNNNNNr   )r@   rA   rB   rC   _tied_weights_keys_tp_plan_pp_planr%   r   r   r/   r   r   r   r)  r   r   r   r   r   r:   rD   rE   rF   s   @r(   r+  r+    s#   *,GH23H_-z:;H  .2.204(,26*.!%-.B
##d*B
 t+B
 &&-	B

 B
 ((4/B
   4'B
 $;B
 ell*B
 +,B
 
 B
  B
r*   r+  )r   r  r+  )r   )r   )@r   collections.abcr   typingr   r/   torch.nnr    r   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   masking_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   utils.genericr   r   r   utils.output_capturingr   configuration_nanochatr   r  r   rH   r   r   r   r   r8   r   r   r   r   r   r   r  r+  __all__r   r*   r(   <module>rR     s  *  $    & ! . ) I / 9 O K F & 7 Y Y 5 2!ehhoo !><bii ><B *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2( )*G)		 G) +G)T")) )5 )X o  6 G
+ G
 G
T R
1? R
 R
j Nr*   