tjbddlZddlmZddlmZddlZddlmcmZ ddlmZddl m Z ddl mZddlmZmZdd lmZdd lmZmZdd lmZdd lmZmZdd lmZmZddlm Z m!Z!ddl"m#Z#m$Z$ddl%m&Z&ddl'm(Z(m)Z)m*Z*ddl+m,Z,m-Z-m.Z.ddl/m0Z0ddl1m2Z2Gddej3Z4edGddej5Z6Gddej5Z7Gddej5Z8dej9d e:d!ej9fd"Z; d=d$ej5d%ej9d&ej9d'ej9d(ej9dzd)eed.d>d/Z?Gd0d1ej5Z@Gd2d3eZAe)Gd4d5e$ZBe)Gd6d7eBZCe)Gd8d9eBeZDGd:d;eeBZEgd<ZFdS)?N)Callable)Optional)nn)initialization)ACT2FN)Cache DynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hub)create_causal_mask) GenericForSequenceClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)is_flash_attention_requestedmaybe_autocastmerge_with_config_defaults)capture_outputs)MiniCPM3Configc PeZdZdZd dedededeffd Zdejffd Z xZ S) MiniCPM3ScaledWordEmbeddingz\ This module overrides nn.Embeddings' forward by multiplying with embeddings scale. ?num_embeddings embedding_dim padding_idx embed_scalect|||||_|dt j|ddS)Nr'F persistent)super__init__scalar_embed_scaleregister_buffertorchtensor)selfr$r%r&r' __class__s l/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/minicpm3/modeling_minicpm3.pyr,z$MiniCPM3ScaledWordEmbedding.__init__3sS  DDD"- ]EL,E,ERWXXXXX input_idsct||j|jjzSN)r+forwardr'toweightdtype)r1r5r2s r3r8z#MiniCPM3ScaledWordEmbedding.forward8s4wwy))D,<,?,? @Q,R,RRRr4)r#) __name__ __module__ __qualname____doc__intfloatr,r/Tensorr8 __classcell__r2s@r3r"r".sYYsY3YSY_dYYYYYY SSSSSSSSSSSr4r"RMSNormcTeZdZd deddffd ZdejdejfdZdZxZ S) MiniCPM3RMSNormư>epsreturnNcttjt j||_||_dS)z> MiniCPM3RMSNorm is equivalent to T5LayerNorm N)r+r,r Parameterr/onesr:variance_epsilon)r1 hidden_sizerIr2s r3r,zMiniCPM3RMSNorm.__init__>sD l5:k#:#:;; #r4 hidden_statesc|j}|tj}|ddd}|tj||jzz}|j||zS)NT)keepdim) r;r9r/float32powmeanrsqrtrNr:)r1rP input_dtypevariances r3r8zMiniCPM3RMSNorm.forwardFs|#) %((77  $$Q'',,R,>>% Ht?T4T(U(UU {]--k::::r4cHt|jjd|jS)Nz, eps=)tupler:shaperN)r1s r3 extra_reprzMiniCPM3RMSNorm.extra_reprMs& )**II$2GIIIr4)rH) r<r=r>rAr,r/rBr8r^rCrDs@r3rGrG<s$$$$$$$$$$;U\;el;;;;JJJJJJJr4rGc eZdZUejed<d deffd Ze d dedzde dde dzde d e ffd Z ejed ZxZS)MiniCPM3RotaryEmbeddinginv_freqNconfigct|j|_|j|_||_|jjd|_|j}|jdkrt|j}||j|\}|_ | d|d| d| ddS)N rope_typedefaultraFr)original_inv_freq) r+r,max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrbrope_parametersrdcompute_default_rope_parametersrattention_scalingr.clone)r1rbdevice rope_init_fnrar2s r3r,z MiniCPM3RotaryEmbedding.__init__Ts "("@$*$B! 4[A!%!E >Y & &.t~>L+7< V+L+L($( ZeDDD 0(..2B2BuUUUUUr4rnz torch.deviceseq_lenrJz torch.Tensorc|jd}t|ddp|j|jz}d}d|t jd|dtj|tj|z zz }||fS) a Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). rope_thetahead_dimNr#rrRr;)rnr;) rjgetattrrOnum_attention_headsr/arangeint64r9rA)rbrnrpbasedimattention_factorras r3rkz7MiniCPM3RotaryEmbedding.compute_default_rope_parametersds&%l3fj$//c63EIc3c U\!S!5;???BB&X]XcBddgjj k )))r4cN|jddddf|jddd|j}|dddddf}t |jjtr|jjdkr |jjnd}t|d5||z dd}tj ||fd }| |jz}||jz} dddn #1swxYwY||j | |j fS) NrrSrmpscpuF) device_typeenabledrRrzrt)rarAexpandr]r9rn isinstancetypestrr transposer/catcosrlsinr;) r1x position_idsinv_freq_expandedposition_ids_expandedrfreqsembrrs r3r8zMiniCPM3RotaryEmbedding.forwards!M$4-8>>@@GG HZ[\H]_acdeehhijiqrr ,QQQaaaZ 8 > > @ @'1!(-'E'Ek!(-[`J`J`ahmmfk  U C C C 5 5&,,..1F1L1L1N1NNYYZ[]^__E)UEN333C''))d44C''))d44C  5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 vvAGv$$cff17f&;&;;;s BE&&E*-E*r7)NNN)r<r=r>r/rB__annotations__r r, staticmethodrr@r\rArkno_gradrr8rCrDs@r3r`r`QslVV~VVVVVV (,+/"**%*(*t* ~u$ % ***\*:U]__ < <_ < < < < r,r8rCrDs@r3rrsG00000r4rrPn_reprJc|j\}}}}|dkr|S|dddddddddf|||||}||||z||S)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) rN)r]rreshape)rPrbatchnum_key_value_headsslenrss r3 repeat_kvrs 2?1D.E h zz!!!!QQQaaa"23::5BUW\^bdlmmM  (;e(CT8 T TTr4modulequerykeyvalueattention_maskscalingdropoutkwargsc t||j}t||j} tj||dd|z} || |z} t j| dtj |j } t j | ||j } tj| | } | dd } | | fS)NrRrrS)rzr;)ptrainingr)rnum_key_value_groupsr/matmulrr functionalsoftmaxrUr9r;rr contiguous) rrrrrrrr key_states value_states attn_weights attn_outputs r3eager_attention_forwardrs3 ;<