^jHddlZddlmZddlmZddlmZddlmZddl 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__p/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/glm4/modular_glm4.pyrr#rrcDeZdZdedeffd Z ddejdejdzdejdzde dzd e dzd e ejejfdzd e e d e eje ejejfdzffd ZxZS)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_cacheposition_embeddingskwargsreturnc |}|j|}|jd||||||d|\}} |j|}||z}|}|j|}|j |}|j |}||z}|S)N)r1r2r3r4r5r6r)r+r(r-r,r)r.) r/r1r2r3r4r5r6r7residual_s rforwardzGlm4DecoderLayer.forward3s!,,]; )4>> ')%+ 3    q55mD  =0  55mD / // >  =0 r)NNNFN)rrrrintr%torchTensor LongTensorrbooltuplerr FloatTensorr< __classcell__r0s@rrr's [z [c [/304(,!&HL|| t+&&-    $; #5<<#=>E-. u  %(9(95;L;L(L"MPT"TT Urrc eZdZy)r'Nrrrrr'r'Urrr'c4eZdZdeedeezffd ZxZS)Glm4ForCausalLM super_kwargsr8c "t|di|S)ah labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, Glm4ForCausalLM >>> model = Glm4ForCausalLM.from_pretrained("THUDM/GLM-4-9B-0414") >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/GLM-4-9B-0414") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```r)r$r<)r/rIr0s rr<zGlm4ForCausalLM.forwardZs4w...r) rrrrr rBrr<rDrEs@rrHrHYs+/12/ ' '//rrHc eZdZy)Glm4ForSequenceClassificationNrrrrrLrLwrrrLc eZdZy)Glm4ForTokenClassificationNrrrrrNrN{rrrN)Glm4PreTrainedModel Glm4ModelrHrLrN)$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'rHrLrN__all__rrrr_s  B96&0tt(*&   H %+ g +1+\ L /n/< $@  !:  r