
    <hR                     D   S SK JrJr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  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  SSKJ r   SSK!J"r"J#r#J$r$  SSK%J&r&  SSK'J(r(   " S S\RR                  5      r*S\RV                  S\,S\RV                  4S jr- S7S\RR                  S\RV                  S\RV                  S\RV                  S\\RV                     S\.S\.S\ \"   4S  jjr/S! r0S8S" jr1 " S# S$\RR                  5      r2\" S%5       " S& S'\RR                  5      5       r3 " S( S)\RR                  5      r4 " S* S+\5      r5\# " S, S-\5      5       r6\# " S. S/\65      5       r7\# " S0 S1\6\5      5       r8 " S2 S3\\65      r9 " S4 S5\\65      r:/ S6Qr;g)9    )CallableOptionalUnionN   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)check_model_inputs   )	GlmConfigc                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )GlmMLP.   c                    > [         TU ]  5         Xl        [        R                  " UR
                  SUR                  -  SS9U l        [        R                  " UR                  UR
                  SS9U l        [        UR                     U l        g )N   Fbias)super__init__confignnLinearhidden_sizeintermediate_sizegate_up_proj	down_projr   
hidden_actactivation_fnselfr&   	__class__s     \/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/glm/modeling_glm.pyr%   GlmMLP.__init__/   sn    IIf&8&8!f>V>V:V]bc6#;#;V=O=OV[\#F$5$56    hidden_statesreturnc                     U R                  U5      nUR                  SSS9u  p2X R                  U5      -  nU R                  U5      $ )Nr!   dim)r+   chunkr.   r,   )r0   r5   	up_statesgates       r2   forwardGlmMLP.forward7   sH    %%m4	#//!/4 2 24 88	~~i((r4   )r.   r&   r,   r+   )
__name__
__module____qualname____firstlineno__r%   torchFloatTensorr>   __static_attributes____classcell__r1   s   @r2   r   r   .   s,    7)U%6%6 )5;L;L ) )r4   r   r5   n_repr6   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)shapeexpandreshape)r5   rI   batchnum_key_value_headsslenhead_dims         r2   	repeat_kvrR   @   s_    
 2?1D1D.Ez!!Qa"23::5W\dlmM  e(CTTTr4   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"  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$ )Nr!   r   r8   )r:   dtype)ptrainingr   )rR   num_key_value_groupsrD   matmul	transposerK   r'   
functionalsoftmaxfloat32tor]   rY   r_   
contiguous)rS   rT   rU   rV   rW   rX   rY   rZ   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r2   eager_attention_forwardrm   L   s     3 ; ;<JU$?$?@L<<';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#1==((2U]](SVVW\WbWbcL==((6??([L,,|:K''1-88:K$$r4   c                 x    U SSSS24   nU SSSS24   n[         R                  " U* U4SS9R                  S5      $ )	z*Rotates half the hidden dims of the input..r   Nr!   r   r8   r9   r\   )rD   stackflatten)xx1x2s      r2   rotate_halfrt   f   sJ    	
319B	
319B;;Ryb)11"55r4   c                    UR                  U5      nUR                  U5      nUSSUR                  S   S-  24   R                  SSS9nUSSUR                  S   S-  24   R                  SSS9nUR                  S   nU SSU24   U SUS24   pUSSU24   USUS24   pXr-  [        U5      U-  -   nX-  [        U	5      U-  -   n[        R
                  " X/SS9n[        R
                  " X/SS9nX4$ )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.
.Nr8   r!   r9   )	unsqueezerK   repeat_interleavert   rD   cat)qkcossinposition_idsunsqueeze_dim
rotary_dimq_rotq_passk_rotk_passq_embedk_embeds                r2   apply_rotary_pos_embr   m   s6   ( --
&C
--
&C c'SYYr]a'''
(
:
:1"
:
EC
c'SYYr]a'''
(
:
:1"
:
EC 2Jc;J;&'3
+;)<6c;J;&'3
+;)<6 {{51C78G{{51C78G ii)r2Gii)r2Gr4   c                   (  ^  \ rS rSrSrSS\S\\   4U 4S jjjr  SS\	R                  S\\	R                  \	R                  4   S\\	R                     S	\\   S
\\	R                     S\\   S\\	R                  \	R                  4   4S jjrSrU =r$ )GlmAttention   z=Multi-headed attention from 'Attention Is All You Need' paperr&   	layer_idxc                 <  > [         TU ]  5         Xl        X l        [	        USUR
                  UR                  -  5      U l        UR                  UR                  -  U l	        U R                  S-  U l
        UR                  U l        SU l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR
                  UR                  U R                  -  UR                  S9U l        [        R                  " UR                  U R                  -  UR
                  SS9U l        g )NrQ   g      Tr"   F)r$   r%   r&   r   getattrr)   num_attention_headsrQ   rO   r`   rX   attention_dropout	is_causalr'   r(   attention_biasq_projk_projv_projo_projr0   r&   r   r1   s      r2   r%   GlmAttention.__init__   s@   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JFL^L^ejkr4   r5   position_embeddingsrW   past_key_valuecache_positionrZ   r6   c                 4   UR                   S S n/ UQSPU R                  P7nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
U R                  U5      R                  U5      R	                  SS5      nUu  p[        XX5      u  pUb$  XUS.nUR                  XU R                  U5      u  p[        nU R                  R                  S:w  a  [        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"                  " / UQSP76 R%                  5       nU R'                  U5      nUU4$ )Nr8   r   r!   )r|   r{   r   eager        )rY   rX   )rK   rQ   r   viewrb   r   r   r   updater   rm   r&   _attn_implementationr   r_   r   rX   rM   rg   r   )r0   r5   r   rW   r   r   rZ   input_shapehidden_shapequery_statesrh   ri   r{   r|   cache_kwargsattention_interfacerl   rj   s                     r2   r>   GlmAttention.forward   s    $))#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#[ %#&nUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ "));;;;FFHkk+.L((r4   )r   r&   rQ   r   r   r   r`   r   r   rX   r   N)NN)r@   rA   rB   rC   __doc__r   r   intr%   rD   Tensortupler   
LongTensorr   r   r>   rF   rG   rH   s   @r2   r   r      s    Gly lXc] l l4 +/59))||)) #5<<#=>)) !.	))
 !)) !!1!12)) +,)) 
u||U\\)	*)) ))r4   r   RMSNormc                   8   ^  \ rS rSrSU 4S jjrS rS rSrU =r$ )
GlmRMSNorm   c                    > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        X l        g)z)
GlmRMSNorm is equivalent to T5LayerNorm
N)r$   r%   r'   	ParameterrD   onesweightvariance_epsilon)r0   r)   epsr1   s      r2   r%   GlmRMSNorm.__init__   s/     	ll5::k#:; #r4   c                    UR                   nUR                  [        R                  5      nUR	                  S5      R                  SSS9nU[        R                  " X0R                  -   5      -  nU R                  UR                  U5      -  $ )Nr!   r8   T)keepdim)	r]   rf   rD   re   powmeanrsqrtr   r   )r0   r5   input_dtypevariances       r2   r>   GlmRMSNorm.forward   sw    #))%((7 $$Q',,R,>%H?T?T4T(UU{{]--k:::r4   c                 ^    [        U R                  R                  5       SU R                   3$ )Nz, eps=)r   r   rK   r   r0   s    r2   
extra_reprGlmRMSNorm.extra_repr   s*    ))*+6$2G2G1HIIr4   )r   r   )gư>)	r@   rA   rB   rC   r%   r>   r   rF   rG   rH   s   @r2   r   r      s    $;J Jr4   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$ )GlmRotaryEmbedding   r&   c                   > [         TU ]  5         [        US5      (       aZ  [        UR                  [
        5      (       a;  UR                  R                  SUR                  R                  S5      5      U l        OSU l        UR                  U l	        UR                  U l
        Xl        [        U R                     U l        U R                  U R                  U5      u  o0l        U R                  SUSS9  U R                   U l        g )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r$   r%   hasattr
isinstancer   dictgetr   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr&   r   rope_init_fnattention_scalingregister_bufferr   original_inv_freq)r0   r&   devicer   r1   s       r2   r%   GlmRotaryEmbedding.__init__   s    6>**z&:M:Mt/T/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q((ZeD!%r4   c                 b   U R                   S S S 2S 4   R                  5       R                  UR                  S   SS5      R	                  UR
                  5      nUS S 2S S S 24   R                  5       n[        UR
                  R                  [        5      (       a0  UR
                  R                  S:w  a  UR
                  R                  OSn[        R                  " USS9   UR                  5       UR                  5       -  R                  SS5      n[        R                  " Xf4SS	9nUR                  5       U R                  -  nUR                  5       U R                  -  n	S S S 5        WR	                  UR                   S
9W	R	                  UR                   S
94$ ! , (       d  f       N@= f)Nr   r8   r   mpscpuF)device_typeenabledr!   r9   )r]   )r   floatrL   rK   rf   r   r   r   strrD   autocastrb   rx   r{   r   r|   r]   )
r0   rq   r}   inv_freq_expandedposition_ids_expandedr   freqsembr{   r|   s
             r2   r>   GlmRotaryEmbedding.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   $BF  
F.)r   r&   r   r   r   r   r   r   )r@   rA   rB   rC   r   r%   rD   no_gradr   r>   rF   rG   rH   s   @r2   r   r      s6    /y / /" ]]_<  <r4   r   c                   8  ^  \ rS rSrS\S\4U 4S jjr      SS\R                  S\	\R                     S\	\R                     S\	\   S	\	\   S
\	\R                     S\	\\R                  \R                  4      S\\   S\\R                     4S jjrSrU =r$ )GlmDecoderLayeri  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$   r%   r)   r   	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormr   s      r2   r%   GlmDecoderLayer.__init__  si    !--%VI&>)&*<*<&BUBUV(263E3E6K^K^(_%r4   r5   rW   r}   r   	use_cacher   r   rZ   r6   c                     Un	U R                  U5      nU R                  " SUUUUUUUS.UD6u  pX-   nUn	U R                  U5      nU R                  U5      nX-   nU$ )N)r5   rW   r}   r   r   r   r    )r   r   r   r   )r0   r5   rW   r}   r   r   r   r   rZ   residual_s              r2   r>   GlmDecoderLayer.forward  s     !,,];>> 	
')%)) 3	
 	
 !0 !55mD/ 0r4   )r)   r   r   r   r   )NNNFNN)r@   rA   rB   rC   r   r   r%   rD   r   r   r   r   boolr   r   r   r>   rF   rG   rH   s   @r2   r   r     s    `y `S ` 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	 r4   r   c                   R    \ rS rSr% \\S'   SrSrS/rS/r	Sr
SrSrSrSr\\S.rSrg	)
GlmPreTrainedModeli?  r&   modelTr   past_key_values)r5   
attentionsr   N)r@   rA   rB   rC   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsrF   r   r4   r2   r   r   ?  sQ    &*#*+#4"5N!"&("r4   r   c                     ^  \ rS rSrS\4U 4S jjr\\       SS\\	R                     S\\	R                     S\\	R                     S\\   S\\	R                     S	\\	R                     S
\\   S\\   S\4S jj5       5       rSrU =r$ )GlmModeliR  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                  " [        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$   r%   pad_token_idpadding_idx
vocab_sizer'   	Embeddingr)   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normr   
rotary_embgradient_checkpointing	post_initr   s      r2   r%   GlmModel.__init__T  s     !.. ++LL):):F<N<NPTP`P`ammAFvG_G_A`aA`I_V/A`a
 v11v7J7JK	,F;&+# 	 bs   C>	input_idsrW   r}   r   inputs_embedsr   r   rZ   r6   c           
      8   US L US L-  (       a  [        S5      eUc  U R                  U5      nU(       a  Uc
  [        5       nUcD  Ub  UR                  5       OSn	[        R
                  " XUR                  S   -   UR                  S9nUc  UR                  S5      n[        U R                  UUUUUS9n
UnU R                  X5      nU R                  S U R                  R                    H  nU" U4U
UUUUS.UD6nM     U R                  U5      n[        UUS9$ )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )r   )r&   input_embedsrW   r   r   r}   )rW   r}   r   r   r   )last_hidden_stater   )
ValueErrorr  r	   get_seq_lengthrD   arangerK   r   rv   r   r&   r  r  r  r  r   )r0   r  rW   r}   r   r  r   r   rZ   past_seen_tokensrk   r5   r   decoder_layers                 r2   r>   GlmModel.forwardd  sK    -t";<YZZ *.*;*;I*FM0*nO!CRC^==?de+0<< ]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oomJ![[)H4;;+H+HIM)*).-$7 M J 		-0&++
 	
r4   )r  r  r  r  r  r  r  )NNNNNNN)r@   rA   rB   rC   r   r%   r   r   r   rD   r   r   r   rE   r   r   r   r   r>   rF   rG   rH   s   @r2   r
  r
  R  s    y    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r4   r
  c                   ~  ^  \ rS rSrS/rSS0rSS/S/40rU 4S jrS rS	 r	\
\         SS
\\R                     S\\R                     S\\R                     S\\   S\\R"                     S\\R                     S\\   S\\R                     S\\\R                  4   S\\   S\4S jj5       5       rSrU =r$ )GlmForCausalLMi  zlm_head.weightlm_headcolwise_repr5   logitsc                    > [         TU ]  U5        [        U5      U l        UR                  U l        [
        R                  " UR                  UR                  SS9U l        U R                  5         g )NFr"   )
r$   r%   r
  r   r  r'   r(   r)   r'  r  r/   s     r2   r%   GlmForCausalLM.__init__  sU     f%
 ++yy!3!3V5F5FUS 	r4   c                     Xl         g r   r   )r0   decoders     r2   set_decoderGlmForCausalLM.set_decoder  s    
r4   c                     U R                   $ r   r-  r   s    r2   get_decoderGlmForCausalLM.get_decoder  s    zzr4   r  rW   r}   r   r  labelsr   r   logits_to_keeprZ   r6   c
                 ~   U R                   " SUUUUUUUS.U
D6nUR                  n[        U	[        5      (       a  [	        U	* S5      OU	nU R                  USS2USS24   5      nSnUb)  U R                  " SXU R                  R                  S.U
D6n[        UUUR                  UR                  UR                  S9$ )ac  
Example:

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

>>> model = GlmForCausalLM.from_pretrained("meta-glm/Glm-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-glm/Glm-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  rW   r}   r   r  r   r   N)r)  r4  r  )lossr)  r   r5   r   r   )r   r  r   r   slicer'  loss_functionr&   r  r   r   r5   r   )r0   r  rW   r}   r   r  r4  r   r   r5  rZ   outputsr5   slice_indicesr)  r7  s                   r2   r>   GlmForCausalLM.forward  s    @ ,0:: 	,
)%+')	,
 	,
  118B>SV8W8W~ot4]kmA}a,?@A%%pVt{{OeOepiopD%#33!//))
 	
r4   )r'  r   r  )	NNNNNNNNr   )r@   rA   rB   rC   _tied_weights_keys_tp_plan_pp_planr%   r/  r2  r   r   r   rD   r   r   r   rE   r   r   r   r   r   r   r>   rF   rG   rH   s   @r2   r&  r&    s:   *+=)H_-z:;H  151537+/59-1$(59348
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 ))*8
 D>8
 !!1!128
 c5<</08
 +,8
 
 8
  8
r4   r&  c                       \ rS rSrSrg)GlmForSequenceClassificationi  r   Nr@   rA   rB   rC   rF   r   r4   r2   rA  rA        r4   rA  c                       \ rS rSrSrg)GlmForTokenClassificationi  r   NrB  r   r4   r2   rE  rE    rC  r4   rE  )r   r
  r&  rA  rE  )r   )Nr   )<typingr   r   r   rD   torch.nnr'   activationsr   cache_utilsr   r	   
generationr
   integrationsr   masking_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   configuration_glmr   Moduler   r   r   rR   r   rm   rt   r   r   r   r   r   r   r
  r&  rA  rE  __all__r   r4   r2   <module>rW     s  , - ,   ! . ) 7 / 
 P K F & I I / ()RYY )$	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%46'TA)299 A)H Y'J J (J(< <D*0 *Z   $ K
! K
 K
\ N
' N
 N
b	#CEW 		 =?Q 	r4   