
    Z j0T                     z   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	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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 r0\" S5      S:S j5       r1S\Rd                  S\3S\Rd                  4S jr4 S;S\R\                  S \Rd                  S!\Rd                  S"\Rd                  S#\Rd                  S-  S$\5S%\5S&\"\$   4S' jjr6\" \15       " S( S)\R\                  5      5       r7\" S*5       " S+ S,\R\                  5      5       r8 " S- S.\R\                  5      r9 " S/ S0\5      r:\% " S1 S2\ 5      5       r; " S3 S4\5      r<\% " S5 S6\;5      5       r=\% " S7 S8\;\5      5       r>/ S9Qr?g)<    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hubuse_kernelized_func)create_causal_mask!create_sliding_window_causal_mask)FlashAttentionKwargs)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )	CwmConfigc                      ^  \ rS rSr% \R
                  \S'   SS\4U 4S jjjr\	   SS\S-  S\
S   S\S-  S	\S
\4   4S jj5       r\R                  " 5       \S 5       5       rSrU =r$ )CwmRotaryEmbedding-   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defaultr$   F)
persistentoriginal_inv_freq)super__init__max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr%   rope_parametersr'   compute_default_rope_parametersr   attention_scalingregister_bufferclone)selfr%   devicerope_init_fnr$   	__class__s        u/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/cwm/modeling_cwm.pyr,   CwmRotaryEmbedding.__init__0   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L((ZeD0(..2BuU    r6   ztorch.deviceseq_lenreturnztorch.Tensorc           	         U R                   S   n[        U SS5      =(       d    U R                  U R                  -  nSnSU[        R
                  " SUS[        R                  S9R                  U[        R                  S9U-  -  -  nXe4$ )	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      dtype)r6   rC   )	r0   getattrhidden_sizenum_attention_headstorcharangeint64tofloat)r%   r6   r<   basedimattention_factorr$   s          r9   r1   2CwmRotaryEmbedding.compute_default_rope_parameters@   s    & %%l3fj$/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$ ! , (       d  f       N@= f)Nr   r   mpscpuF)device_typeenabledrA   rM   rB   )r$   rK   expandshaperJ   r6   
isinstancetypestrr   	transposerG   catcosr2   sinrC   )
r5   xposition_idsinv_freq_expandedposition_ids_expandedrT   freqsembr^   r_   s
             r9   forwardCwmRotaryEmbedding.forward^   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#)r2   r%   r.   r/   r'   N)NNN)__name__
__module____qualname____firstlineno__rG   Tensor__annotations__r    r,   staticmethodr   inttuplerK   r1   no_gradr   rf   __static_attributes____classcell__r8   s   @r9   r"   r"   -   s    llVy V V  #'+/"*D *(* t* 
~u$	%	* *: ]]_<  <r;   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$ )z*Rotates half the hidden dims of the input..NrQ   rA   rV   )rX   rG   r]   )r`   x1x2s      r9   rotate_halfry   n   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r;   rotary_pos_embc                     UR                  U5      nUR                  U5      nX-  [        U 5      U-  -   nX-  [        U5      U-  -   nXV4$ )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.
)	unsqueezery   )qkr^   r_   unsqueeze_dimq_embedk_embeds          r9   apply_rotary_pos_embr   u   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)rX   rW   reshape)r   r   batchnum_key_value_headsslenr@   s         r9   	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$ )NrA   r   rQ   )rM   rC   )ptrainingr   )r   num_key_value_groupsrG   matmulr\   r   
functionalsoftmaxfloat32rJ   rC   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r9   eager_attention_forwardr      s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r;   c                     ^  \ 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\\   S\
\R                  \R                  S-  4   4S jjrSrU =r$ )CwmAttention   z=Multi-headed attention from 'Attention Is All You Need' paperr%   	layer_idxc                   > [         TU ]  5         [        US5      (       a  UR                  U   OS U l        Xl        X l        [        USUR                  UR                  -  5      U l
        UR                  UR                  -  U l        U R                  S-  U l        UR                  U l        SU l        [         R"                  R%                  UR                  UR                  U R                  -  SS9U l        [         R"                  R%                  UR                  UR                  U R                  -  SS9U l        [         R"                  R%                  UR                  UR                  U R                  -  SS9U l        ["        R$                  " UR                  U R                  -  UR                  SS9U l        U R                  S:X  a  UR.                  U l        g S U l        g )Nlayer_typesr@   g      TFbiassliding_attention)r+   r,   hasattrr   
layer_typer%   r   rD   rE   rF   r@   r   r   r   attention_dropout	is_causalrG   r   Linearq_projk_projv_projo_projsliding_windowr5   r%   r   r8   s      r9   r,   CwmAttention.__init__   s}   ;B6=;Y;Y&,,Y7_c"
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9hhoof&8&8&:T:TW[WdWd:dkpoqhhoof&8&8&:T:TW[WdWd:dkpoqhhoof&8&8&:T:TW[WdWd:dkpoqii : :T]] JFL^L^ejk7;J]7]f33cgr;   Nr   position_embeddingsr   past_key_valuesr   r=   c                    UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
Uu  p[        XX5      u  pUb  UR                  XU R                  5      u  p[        R                  " U R                  R                  [        5      nU" U UU	U
U4U R                  (       d  SOU R                   U R"                  U R$                  S.UD6u  pUR&                  " / UQSP76 R)                  5       nU R+                  U5      nX4$ )NrQ   r   rA           )r   r   r   )rX   r@   r   viewr\   r   r   r   updater   r   get_interfacer%   _attn_implementationr   r   r   r   r   r   r   r   )r5   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   r^   r_   attention_interfacer   r   s                   r9   rf   CwmAttention.forward   s    $))#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#[ &'6'='=jX\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL..
%
 
%
! "));;;;FFHkk+.((r;   )r   r%   r@   r   r   r   r   r   r   r   r   r   r   rh   )ri   rj   rk   rl   __doc__r    rp   r,   rG   rm   rq   r   r   r   rf   rs   rt   ru   s   @r9   r   r      s    Ghy hS h* )-')||') #5<<#=>') t+	')
 ') -.') 
u||U\\D00	1') ')r;   r   RMSNormc                   x   ^  \ rS rSrS
S\SS4U 4S jjjrS\R                  S\R                  4S jrS r	S	r
U =r$ )
CwmRMSNorm   epsr=   Nc                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z)
CwmRMSNorm is equivalent to T5LayerNorm
N)r+   r,   r   	ParameterrG   onesweightvariance_epsilon)r5   rE   r   r8   s      r9   r,   CwmRMSNorm.__init__   s/     	ll5::k#:; #r;   r   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )NrA   rQ   T)keepdim)	rC   rJ   rG   r   powmeanrsqrtr   r   )r5   r   input_dtypevariances       r9   rf   CwmRMSNorm.forward   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r;   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)rq   r   rX   r   )r5   s    r9   
extra_reprCwmRMSNorm.extra_repr  s*    ))*+6$2G2G1HIIr;   )r   r   )gư>)ri   rj   rk   rl   rK   r,   rG   rm   rf   r   rs   rt   ru   s   @r9   r   r      sB    $ $$ $ $;U\\ ;ell ;J Jr;   r   c                   .   ^  \ rS rSrU 4S jrS rSrU =r$ )CwmMLPi  c                   > [         TU ]  5         Xl        UR                  U l        UR                  U l        [
        R                  " U R                  U R                  UR                  S9U l        [
        R                  " U R                  U R                  UR                  S9U l	        [
        R                  " U R                  U R                  UR                  S9U l
        [        UR                     U l        g )Nr   )r+   r,   r%   rE   intermediate_sizer   r   mlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr5   r%   r8   s     r9   r,   CwmMLP.__init__  s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r;   c                     U R                  U R                  U R                  U5      5      U R                  U5      -  5      nU$ rh   )r   r   r   r   )r5   r`   r   s      r9   rf   CwmMLP.forward  s6    NN4;;t~~a/@#ADLLQRO#ST	r;   )r   r%   r   r   rE   r   r   )ri   rj   rk   rl   r,   rf   rs   rt   ru   s   @r9   r   r     s    0 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$ )CwmDecoderLayeri  r%   r   c                   > [         TU ]  5         UR                  U l        [        XS9U l        [        U5      U l        [        UR                  UR                  S9U l	        [        UR                  UR                  S9U l
        g )N)r%   r   r   )r+   r,   rE   r   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r9   r,   CwmDecoderLayer.__init__  si    !--%VI&>)&*<*<&BUBUV(263E3E6K^K^(_%r;   Nr   r   ra   r   	use_cacher   r   r=   c           
          UnU R                  U5      nU R                  " SUUUUUUS.UD6u  pX-   nUnU R                  U5      nU R                  U5      nX-   nU$ )N)r   r   ra   r   r   r    )r   r   r   r   )
r5   r   r   ra   r   r   r   r   residual_s
             r9   rf   CwmDecoderLayer.forward!  s     !,,];>> 
')%+ 3
 
 !0 !55mD/ 0r;   )rE   r   r   r   r   )NNNFN)ri   rj   rk   rl   r    rp   r,   rG   rm   
LongTensorr   boolrq   r   r   rf   rs   rt   ru   s   @r9   r   r     s    `y `S ` /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
 r;   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
CwmPreTrainedModeliA  r%   modelTr   r   )r   
attentionsr   N)ri   rj   rk   rl   r    rn   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrs   r   r;   r9   r   r   A  sQ    &*#*+#4"5N!"&("r;   r   c                       \ rS rSrSrg)CwmModelOutputWithPastiT  r   N)ri   rj   rk   rl   rs   r   r;   r9   r	  r	  T  s    r;   r	  c                     ^  \ rS rSr\rS\4U 4S jjr\\\	      SS\
R                  S-  S\
R                  S-  S\
R                  S-  S\S-  S	\
R                  S-  S
\S-  S\\   S\4S jj5       5       5       rSrU =r$ )CwmModeliX  r%   c           	        > [         TU ]  U5        UR                  U l        UR                  U l        [
        R                  " UR                  UR                  U R                  5      U l        [        R
                  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        [%        US9U l        SU l        U R+                  5         g s  snf )Nr   r%   F)r+   r,   pad_token_idpadding_idx
vocab_sizer   	EmbeddingrE   embed_tokensrG   
ModuleListrangenum_hidden_layersr   layersr   r   normr"   
rotary_embgradient_checkpointing	post_initr   s      r9   r,   CwmModel.__init__\  s     !.. ++LL):):F<N<NPTP`P`ahh))AFvG_G_A`aA`I_V/A`a
 v11v7J7JK	,F;&+# 	 bs   DN	input_idsr   ra   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=n	[        5      (       d9  U R                  UUUUS.n
U
R                  5       n[        S
0 U
D6[        S
0 UD6S.n	UnU R                  X5      n[!        U R"                  S U R                  R$                   5       H,  u  pU" U4XR                  R&                  U      UUUS.UD6nM.     U R)                  U5      n[+        UUS	9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr  r   r   )r6   )r%   r  r   r   ra   )full_attentionr   )r   ra   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   r%   get_seq_lengthrG   rH   rX   r6   r|   rY   dictcopyr   r   r  	enumerater  r  r   r  r	  )r5   r  r   ra   r   r  r   r   past_seen_tokenscausal_mask_mappingmask_kwargssliding_mask_kwargsr   r   idecoder_layers                   r9   rf   CwmModel.forwardl  s    -t";<YZZ *.*;*;I*FM0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L?-FF++!."0#2 ,K #."2"2"4 #5"C{"C%F%]I\%]#
 &"oomJ )$++6U8U8U*V WA)2;;3J3J13MN) /$7 M !X 		-0%++
 	
r;   )r  r  r  r  r  r  r  )NNNNNN)ri   rj   rk   rl   r    config_classr,   r   r   r   rG   r   rm   r   FloatTensorr   r   r   r	  rf   rs   rt   ru   s   @r9   r  r  X  s    Ly     .2.204(,26!%8
##d*8
 t+8
 &&-	8

 8
 ((4/8
 $;8
 +,8
 
 8
    8
r;   r  c                   P  ^  \ rS rSrSS0rSS0rSS/S/40rU 4S jr\\	        SS
\
R                  S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\
R                  S	-  S\
R                  S	-  S\S	-  S\\
R                  -  S\\   S\4S jj5       5       rSrU =r$ )CwmForCausalLMi  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 )NFr   )
r+   r,   r  r   r  r   r   rE   r1  r  r   s     r9   r,   CwmForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FUS 	r;   Nr  r   ra   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$ )ac  
Example:

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

>>> model = CwmForCausalLM.from_pretrained("meta-cwm/Cwm-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-cwm/Cwm-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   ra   r   r  r   N)r3  r6  r  )lossr3  r   r   r   r   )r   r   rY   rp   slicer1  loss_functionr%   r  r   r   r   r   )r5   r  r   ra   r   r  r6  r   r7  r   outputsr   slice_indicesr3  r9  s                  r9   rf   CwmForCausalLM.forward  s    > ,0:: ,
)%+',
 ,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r;   )r1  r   r  )NNNNNNNr   )ri   rj   rk   rl   _tied_weights_keys_tp_plan_pp_planr,   r   r   rG   r   rm   r   r.  r   rp   r   r   r   rf   rs   rt   ru   s   @r9   r0  r0    s   *,GH23H_-z:;H  .2.204(,26*.!%-.6
##d*6
 t+6
 &&-	6

 6
 ((4/6
   4'6
 $;6
 ell*6
 +,6
 
 6
  6
r;   r0  )r   r  r0  )r   )r   )@collections.abcr   typingr   rG   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   r   masking_utilsr   r   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_cwmr    Moduler"   ry   r   rm   rp   r   rK   r   r   r   r   r   r   r	  r  r0  __all__r   r;   r9   <module>rU     s  , %    ! . ) f f R B 9 O K F & I I G 5 (>< ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2 )*:)299 :) +:)z Y'J J (J(RYY  '0 'T   $	4 	 N
! N
 N
b F
' F
 F
R ?r;   