qiHddlZddlmZddlmZddlmZddlmZddl m Z ddl m Z m Z d d lmZmZmZmZd d lmZd d lmZd dlmZe j2eZdZGddeZGddeZGddeZGddeZ GddeZ!GddeZ"gdZ#y)N)Cache)FlashAttentionKwargs)GradientCheckpointingLayer)CausalLMOutputWithPast)Unpack)TransformersKwargslogging) GlmAttentionGlmForCausalLMGlmForSequenceClassificationGlmForTokenClassification)Phi3MLP) Glm4Config) Glm4RMSNormzTHUDM/GLM-4-9B-0414c eZdZy)Glm4MLPN__name__ __module__ __qualname__W/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/glm4/modular_glm4.pyrr#rrcdeZdZdedeffd Z ddejdejdzdejdzde dzd e dzd ejdzd e ejejfdzd e e d e eje ejejfdzffdZxZS)Glm4DecoderLayerconfig layer_idxct||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_attnrmlpr rms_norm_epsinput_layernormpost_attention_layernormpost_self_attn_layernormpost_mlp_layernorm)selfr r! __class__s rr%zGlm4DecoderLayer.__init__(s !--&f J6?*6+=+=6CVCVW(3F4F4FFL_L_(`%(3F4F4FFL_L_(`%"-f.@.@fFYFY"ZrN hidden_statesattention_mask position_idspast_key_values use_cachecache_positionposition_embeddingskwargsreturnc |} |j|}|jd|||||||d|\}} |j|}| |z}|} |j|}|j |}|j |}| |z}|S)N)r1r2r3r4r5r6r7r)r+r(r-r,r)r.) r/r1r2r3r4r5r6r7r8residual_s rforwardzGlm4DecoderLayer.forward3s!,,]; )4>>  ')%+) 3     q55mD  =0  55mD / // >  =0 r)NNNFNN)rrrrintr%torchTensor LongTensorrbooltuplerr FloatTensorr= __classcell__r0s@rrr's [z [c [/304(,!&26HL!||! t+!&&- !  ! $; !((4/!#5<<#=>E!-.! u  %(9(95;L;L(L"MPT"TT U!rrc eZdZy)r'Nrrrrr'r'Wrrr'c4eZdZdeedeezffd ZxZS)Glm4ForCausalLM super_kwargsr9c "t|di|S)ah labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, Glm4ForCausalLM >>> model = Glm4ForCausalLM.from_pretrained("THUDM/GLM-4-9B-0414") >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/GLM-4-9B-0414") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```r)r$r=)r/rJr0s rr=zGlm4ForCausalLM.forward\s4w...r) rrrrr rCrr=rErFs@rrIrI[s+/12/ ' '//rrIc eZdZy)Glm4ForSequenceClassificationNrrrrrMrMyrrrMc eZdZy)Glm4ForTokenClassificationNrrrrrOrO}rrrO)Glm4PreTrainedModel Glm4ModelrIrMrO)$r? cache_utilsrmodeling_flash_attention_utilsrmodeling_layersrmodeling_outputsrprocessing_utilsrutilsr r glm.modeling_glmr r rrphi3.modeling_phi3rconfiguration_glm4r modeling_glm4r get_loggerrlogger_CHECKPOINT_FOR_DOCrrr'rIrMrO__all__rrrr`s  B96&0tt(*&   H %+ g -1-` L /n/< $@  !:  r