
    h                     d   d dl mZmZ d dlZddlmZ ddlmZ ddlm	Z	 ddl
mZ ddlmZ dd	lmZmZ dd
lmZ ddlmZmZmZmZ ddlmZ ddlmZ ddlmZ  ej<                  e      Z dZ! G d de      Z" G d de	      Z# G d de      Z$ G d de      Z% G d de      Z& G d de      Z'g dZ(y)    )OptionalUnionN   )Cache)FlashAttentionKwargs)GradientCheckpointingLayer)CausalLMOutputWithPast)Unpack)TransformersKwargslogging)deprecate_kwarg   )GlmAttentionGlmForCausalLMGlmForSequenceClassificationGlmForTokenClassification)Phi3MLP   )
Glm4Config)Glm4RMSNormzTHUDM/GLM-4-9B-0414c                       e Zd Zy)Glm4MLPN__name__
__module____qualname__     c/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/transformers/models/glm4/modular_glm4.pyr   r   &       r   r   c                       e Zd Zdedef fdZ eddd      	 	 	 	 	 	 ddej                  d	e	ej                     d
e	ej                     de	e   de	e   de	ej                     de	eej                  ej                  f      dee   deej                   e	eej                   ej                   f      f   fd       Z xZS )Glm4DecoderLayerconfig	layer_idxc                    t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        t        |j                  |j                        | _        t        |j                  |j                        | _        y )N)r#   r$   )eps)super__init__hidden_sizeGlm4Attention	self_attnr   mlpr   rms_norm_epsinput_layernormpost_attention_layernormpost_self_attn_layernormpost_mlp_layernorm)selfr#   r$   	__class__s      r   r(   zGlm4DecoderLayer.__init__+   s    !--&f	J6?*6+=+=6CVCVW(3F4F4FFL_L_(`%(3F4F4FFL_L_(`%"-f.@.@fFYFY"Zr   past_key_valuepast_key_valuesz4.58)new_nameversionhidden_statesattention_maskposition_ids	use_cachecache_positionposition_embeddingskwargsreturnc                    |}	| j                  |      } | j                  d|||||||d|\  }}
| j                  |      }|	|z   }|}	| j                  |      }| j	                  |      }| j                  |      }|	|z   }|S )N)r8   r9   r:   r5   r;   r<   r=   r   )r.   r+   r0   r/   r,   r1   )r2   r8   r9   r:   r5   r;   r<   r=   r>   residual_s              r   forwardzGlm4DecoderLayer.forward6   s     !,,];)4>> 	
')%+) 3	
 	
q 55mD =0 55mD///> =0r   )NNNFNN)r   r   r   r   intr(   r   torchTensorr   
LongTensorr   booltupler
   r   FloatTensorrC   __classcell__r3   s   @r   r"   r"   *   s   	[z 	[c 	[ %0A6R 2637+/$)59KO!||! !.! u//0	!
 "%! D>! !!1!12! &eELL%,,,F&GH! -.! 
u  (51B1BEDUDU1U+V"WW	X! S!r   r"   c                       e Zd Zy)r*   Nr   r   r   r   r*   r*   [   r    r   r*   c                   8     e Zd Zdee   deeef   f fdZ xZ	S )Glm4ForCausalLMsuper_kwargsr?   c                 "    t        |   di |S )ah  
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, Glm4ForCausalLM

        >>> model = Glm4ForCausalLM.from_pretrained("THUDM/GLM-4-9B-0414")
        >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/GLM-4-9B-0414")

        >>> 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'   rC   )r2   rP   r3   s     r   rC   zGlm4ForCausalLM.forward`   s    4 w...r   )
r   r   r   r
   r   r   rI   r	   rC   rK   rL   s   @r   rO   rO   _   s0    /12/ 
u,,	-/ /r   rO   c                       e Zd Zy)Glm4ForSequenceClassificationNr   r   r   r   rS   rS   }   r    r   rS   c                       e Zd Zy)Glm4ForTokenClassificationNr   r   r   r   rU   rU      r    r   rU   )Glm4PreTrainedModel	Glm4ModelrO   rS   rU   ))typingr   r   rE   cache_utilsr   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr	   processing_utilsr
   utilsr   r   utils.deprecationr   glm.modeling_glmr   r   r   r   phi3.modeling_phi3r   configuration_glm4r   modeling_glm4r   
get_loggerr   logger_CHECKPOINT_FOR_DOCr   r"   r*   rO   rS   rU   __all__r   r   r   <module>rh      s     #    B 9 6 & 0 0 t t ( * & 
		H	%+ 	g 	.1 .b	L 	/n /<	$@ 		!: 	r   