rh:6ddlZddlmZmZddlZddlmZddlmZddlm Z m Z m Z ddl m Z ddlmZdd lmZdd lmZmZdd lmZmZmZmZdd lmZmZdd lmZmZddl m!Z!ddl"m#Z#ddl$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*ddl+m,Z,e(jZe.Z/Gddej`Z1dZ2d8dZ3dejhde5dejhfdZ6dZ7Gddej`Z8Gdd e8Z9Gd!d"e8Z:ed#Gd$d%ej`Z;e8e9e:d&Z<Gd'd(eZ=e&Gd)d*e!Z>Gd+d,ej`Z?e&Gd-d.e>Z@e&Gd/d0e>e ZAGd1d2ee>ZBGd3d4ee>ZCGd5d6ee>ZDgd7ZEy)9N)OptionalUnion)nn)ACT2FN)Cache DynamicCache StaticCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)_flash_attention_forward!flash_attn_supports_top_left_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)PreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)check_model_inputs)DiffLlamaConfigc$eZdZfdZdZxZS) DiffLlamaMLPct|||_|j|_|j|_t j |j|jd|_t j |j|jd|_t j |j|jd|_ t|j|_ yNFbias) super__init__config hidden_sizeintermediate_sizerLinear gate_projup_proj down_projr hidden_actact_fnselfr) __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/diffllama/modeling_diffllama.pyr(zDiffLlamaMLP.__init__7s  !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV 4#9#94;K;KRWXV../ c|j|j|j||j|z}|SN)r/r1r-r.)r3xr/s r5forwardzDiffLlamaMLP.forwardAs6NN4;;t~~a/@#ADLLQRO#ST r6)__name__ __module__ __qualname__r(r: __classcell__r4s@r5r"r"6s 0r6r"c|dd|jddzf}|d|jddzdf}tj| |fdS)z*Rotates half the hidden dims of the input..Ndim)shapetorchcat)r9x1x2s r5 rotate_halfrJFsZ 3"!''"+"" " #B 3 q " " #B 99rc2YB ''r6c|j|}|j|}||zt||zz}||zt||zz}||fS)aApplies 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. ) unsqueezerJ)qkcossin position_ids unsqueeze_dimq_embedk_embeds r5apply_rotary_pos_embrUMsY( -- &C -- &C3w;q>C/0G3w;q>C/0G G r6 hidden_statesn_repreturnc|j\}}}}|dk(r|S|dddddddddfj|||||}|j|||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)rEexpandreshape)rVrWbatchnum_key_value_headsslenhead_dims r5 repeat_kvr`hso 2?1D1D.E h z!!Qa"23::5BUW\^bdlmM  (;e(CT8 TTr6c>ddtjd|zzz S)Ng?g333333?g333333ӿ)mathexp) layer_idxs r5lambda_init_fnrets txxy 011 11r6c\eZdZdZddedeeffd Z ddejde ejejfdeejdeejd ee d e d eejd e ejeejee ejffd ZxZS)DiffLlamaAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr)rdct|||_||_|-tj d|j jd|j|_|j|_ |j|_ t|d|j|jz|_ |j|_|j|jz|_|j |_|j"|_d|_t'j(|j|j|jz|j*|_t'j(|j|j|jz|j*|_t'j(|j|j|jz|j*|_t'j(|j|jz|j|j*|_t5||_t'j8t;j<d|j>|jf|_ t'j8t;j<d|j>|jf|_!t'j8t;j<d|j>|jf|_"t'j8t;j<d|j>|jf|_#t'jHd|jz|jJd |_&y) NzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.r_Tr%r)sizerBF)epselementwise_affine)'r'r(r)rdlogger warning_oncer4r;attention_dropoutr*num_attention_heads num_headsgetattrr_r]num_key_value_groupsmax_position_embeddings rope_theta is_causalrr,attention_biasq_projk_projv_projo_projre lambda_init ParameterrFnormallambda_std_dev lambda_q1 lambda_k1 lambda_q2 lambda_k2RMSNorm rms_norm_eps groupnormr3r)rdr4s r5r(zDiffLlamaAttention.__init__{s~  "      !8!8 9:,,  "(!9!9!--33 D4D4D4VW #)#=#= $(NNd6N6N$N!'-'E'E$ ++ii 0 0$..4==2PW]WlWlm ii 0 0$2J2JT]]2Zagavavw ii 0 0$2J2JT]]2Zagavavw ii >@P@PW]WlWlm ))4ell1f6K6KSWS`S`Rb&cdell1f6K6KSWS`S`Rb&cdell1f6K6KSWS`S`Rb&cdell1f6K6KSWS`S`Rb&cdA $56;N;Nchir6rVposition_embeddingsattention_maskrQpast_key_value use_cachecache_positionrXc |j\} } } | } |j|} |j|}|j|}| j | | |j |j jdd} |j | | |j|j jdd}|j | | |j|j jdd}|\}}t| |||\} }|'|||d}|j|||j|\}}t||j}t||j}tjtj |ddd}|j#dddd}tj$| |jddt'j(|j z }|#|ddddddd|j*df}||z}t,j.j1|dtj2j5| j6}t,j.j9||j:|j< }tj>tj@|jB|jDzdtj2j5| j6}tj>tj@|jF|jHzdtj2j5| j6}||z |jJz}tj$||}tj |dd\}}|||zz }d|jJz |jM|z}|jddjO}|jQ| | d}|jS|}||fS) NrrBrPrOrrCrArrDdtype)ptraining)*rirwrxryviewrpr_ transposer]rUupdaterdr`rrrFrGchunkrepeatmatmulrbsqrtrEr functionalsoftmaxfloat32tordropoutrnrrcsumrrrrr{r contiguousr[rz)r3rVrrrQrrrkwargsbsz target_len_q_len query_states key_states value_statesrOrP cache_kwargs attn_weights causal_masklambda_1lambda_2 lambda_full attn_output attn_output1 attn_output2s r5r:zDiffLlamaAttention.forwards|+//1Z{{=1 [[/ {{=1 #((eT^^T]]S]]^_abc __S%1I1I4==Yccdeghi #((eT5M5Mt}}]gghiklm &S#7 jRUWZ#[ j  %#&snUL'5'<' %*[[aQ%G" l"[<%?? 4+++t~~k/JJ !++Aq1<<> !))#ub9 kk+. L((r6r8NNNFN)r;r<r=__doc__r rintr(rFTensortuple LongTensorrboolr:r>r?s@r5rgrgxsG j j8C= jL2637*.59<)||<)#5<<#=><)!. <) u//0 <) ! <)<)!!1!12<) u||Xell3XeELL>Q5RR S<)r6rgcJeZdZdZfdZ d dej deej ej fdeejdeejdee de d eejd eej eej eeej ffd Z xZ S) DiffLlamaFlashAttention2aN DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. cBt||i|t|_yr8)r'r(r_flash_attn_uses_top_left_mask)r3argsrr4s r5r(z!DiffLlamaFlashAttention2.__init__s# $)&) /P.Q+r6rVrrrQrrrrXc@ t|tr td|j\}} } |j |} |j |} |j |} | j|| |j|jjdd} | j|| |j|jjdd} | j|| |j|jjdd} |+tjd|j| |\}}n|\}}t| | ||\} } |'|||d}|j!| | |j"|\} } | jdd} | jdd} | jdd} |j$r |j&nd}| j(}| j*j,dk7r| j*j,nd}|t.j0k(rt/j2r:t5t.d rt/j6|nt/j8}nMt5|j:d r|j:j<}n |jj>j(}tjd |d | jA|} | jA|} | jA|} t/jB| dd \}}|jEdddd}|jEdddd}tG| | ||| ||tI|dd|jJ|jL }tG| | ||| ||tI|dd|jJ|jL }t/jN||gd }t/jB|dd \}}t/jPt/jR|jT|jVzdt.j0jA| j(}t/jPt/jR|jX|jZzdt.j0jA| j(}||z |j\z}|||zz }d|j\z |j_|z}|ja|| djc}|je|}|dfS)Nz`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformersrrBaYThe attention layers in this model are transitioning from computing the RoPE embeddings internally through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed `position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be removed and `position_embeddings` will be mandatory.rmpscpuget_autocast_dtype_pre_quantization_dtypezThe input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in .rCsliding_window)rQrruse_top_left_maskrurAr)3 isinstancer ValueErrorrirwrxryrrpr_rr]rlrm rotary_embrUrrdrrnrdevicetyperFris_autocast_enabledhasattrrget_autocast_gpu_dtyper)rweightrrrrrqrrurGrcrrrrrr{rr[rrz)r3rVrrrQrrrrrrrrrrOrPr dropout_rate input_dtype device_type target_dtype value_states1 value_states2rrrrrrs r5r:z DiffLlamaFlashAttention2.forwards nk 2}  &**, UA{{=1 [[/ {{=1 $((eT^^T]]S]]^_abc __S%1I1I4==Yccdeghi #((eT5M5Mt}}]gghiklm  &   G  |\BHC*HC#7 jRUWZ#[ j  %#&snUL'5'<'2E2E2J2Je2Sl))..Y^ %-- '((*u&:;,,[9557 &?@#{{BB #{{1177    >$  (??<8L#|4J'??<8L',{{<'J$ }%,,Q1a8 %,,Q1a8 /     % "4)94@"AAnn  0     % "4)94@"AAnn  ii| <"E %*[[aQ%G" l99UYYt~~'FBV[VcVcdehh    99UYYt~~'FBV[VcVcdehh    )D,<,<< "[<%?? 4+++t~~k/JJ !))#ub9DDF kk+. D  r6r)r;r<r=rr(rFrrrrrrr:r>r?s@r5rrs R6:37*.59B!||B!#5<<#=>B!!!1!12 B! u//0 B! ! B!B!!!1!12B! u||Xell3XeELL>Q5RR SB!r6rc8eZdZdZ d dej deej ej fdeej deejdee de d eejd eej eej eeej ffd Z y) DiffLlamaSdpaAttentiona DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. NrVrrrQrrrrXc |j\} } } |j|} |j|} |j|}| j | | |j |j jdd} | j | | |j|j jdd} |j | | |j|j jdd}|\}}t| | ||\} } |'|||d}|j| ||j|\} }t| |j} t||j}tjtj |ddd}|j#dddd}|}||ddddddd| j$df}| j&j(dk(r2|0| j+} | j+} |j+}|duxr| dkD}tj,j.j1| | |||j2r |j4nd| }tj |dd\}}tj6tj8|j:|j<zdtj> jA| jB}tj6tj8|jD|jFzdtj> jA| jB}||z |jHz}|||zz }d|jHz |jK|z}|jddj+}|j | | d}|jM|}|dfS) NrrBrrCrArcudar) attn_mask dropout_prur)'rirwrxryrrpr_rr]rUrrdr`rrrFrGrrrErrrrrscaled_dot_product_attentionrrnrcrrrrrrrrr{rrz)r3rVrrrQrrrrrrrrrrrOrPrrrurrrrrrs r5r:zDiffLlamaSdpaAttention.forwardxsm&**, UA{{=1 [[/ {{=1 #((eT^^T]]S]]^_abc __S%1I1I4==Yccdeghi #((eT5M5Mt}}]gghiklm &S#7 jRUWZ#[ j  %#&snUL'5'<' !&&sE26 kk+. D  r6r) r;r<r=rrFrrrrrrr:r6r5rrps2637*.59I!||I!#5<<#=>I!!. I! u//0 I! ! I!I!!!1!12I! u||Xell3XeELL>Q5RR SI!r6rrc,eZdZdfd ZdZdZxZS)DiffLlamaRMSNormct|tjt j ||_||_y)z? DiffLlamaRMSNorm is equivalent to T5LayerNorm N)r'r(rr|rFonesrvariance_epsilon)r3r*rjr4s r5r(zDiffLlamaRMSNorm.__init__s1 ll5::k#:; #r6c"|j}|jtj}|j dj dd}|tj ||jzz}|j|j|zS)NrBrAT)keepdim) rrrFrpowmeanrsqrtrr)r3rVrvariances r5r:zDiffLlamaRMSNorm.forwardsy#)) %((7  $$Q',,R,>% Ht?T?T4T(UU {{]--k:::r6c^t|jjd|jS)Nz, eps=)rrrErr3s r5 extra_reprzDiffLlamaRMSNorm.extra_reprs* ))*+6$2G2G1HIIr6)gư>)r;r<r=r(r:rr>r?s@r5rrs$;Jr6r)eagerflash_attention_2sdpac(eZdZdedeffd Z ddejdeejdeejdee dee d eejd ee ejejfd e ed e ejfd ZxZS)DiffLlamaDecoderLayerr)rdc:t||j|_t|j|||_t ||_t|j|j|_ t|j|j|_ y)N)r)rdrj) r'r(r*DIFFLLAMA_ATTENTION_CLASSES_attn_implementation self_attnr"mlprrinput_layernormpost_attention_layernormrs r5r(zDiffLlamaDecoderLayer.__init__sz !--4V5P5PQY_ktu'/0B0BH[H[\(89K9KQWQdQd(e%r6rVrrQrrrrrrXc |} |j|}|jd|||||||d|\}} | |z}|} |j|}|j|}| |z}|S)N)rVrrQrrrrr)rrrr) r3rVrrQrrrrrresidualrs r5r:zDiffLlamaDecoderLayer.forwards!,,]; )4>>  ')%)) 3     q!=0 !55mD /  =0 r6)NNNFNN)r;r<r=r rr(rFrrrrrrrrr:r>r?s@r5rrsff3f2637*.$)59KO||!.u//0  !  D> !!1!12&eELL%,,,F&GH+, u|| r6rc\eZdZUeed<dZdZdgZdgZdZ dZ dZ dZ dZ eedZfdZxZS) DiffLlamaPreTrainedModelr)modelTrpast_key_valuesF)rV attentionsct||t|tr|jj j d|jj|jj j d|jj|jj j d|jj|jj j d|jjyy)Nr) r' _init_weightsrrgrdatanormal_r)r~rrr)r3moduler4s r5rz&DiffLlamaPreTrainedModel._init_weightss f% f0 1    ! ! ) )!T[[-G-G H    ! ! ) )!T[[-G-G H    ! ! ) )!T[[-G-G H    ! ! ) )!T[[-G-G H 2r6)r;r<r=r __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendrrg_can_record_outputsrr>r?s@r5rr s^ &*#01#4"5N!"'.( IIr6rc^eZdZddeffd Zej edZxZ S)DiffLlamaRotaryEmbeddingr)ct|t|drUt|jt r;|jj d|jj d|_nd|_|j|_ |j|_ ||_ t|j|_ |j|j|\}|_|jd|d|j |_y)N rope_scaling rope_typerdefaultinv_freqF) persistent)r'r(rrrdictgetrrsmax_seq_len_cachedoriginal_max_seq_lenr)r rope_init_fnattention_scalingregister_bufferroriginal_inv_freq)r3r)rrr4s r5r(z!DiffLlamaRotaryEmbedding.__init__)s  6> *z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B! /?+/+<+>@GG HZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk ^^ U C 5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C  5 vvAGGv$cff177f&;;;  5 5s BF%%F.r8) r;r<r=r r(rFno_gradrr:r>r?s@r5rr(s3//"U]]_ < r?s@r5r+r+Js 151537+/5959$(8 E,,-8 !.8 u//0 8 "% 8   1 12 8 !!1!128 D>8 +,8  !8 8 r6r+cpeZdZdgZddiZddgdgfiZfdZdZdZe e dd e e jd e e jd e e jd e ed e e j de e jde ede e jdeee jfdeedefdZxZS)DiffLlamaForCausalLMzlm_head.weightlm_head colwise_reprVlogitsct||t||_|j|_t j |j|jd|_|jyr$) r'r(r+rr/rr,r*rEr8r2s r5r(zDiffLlamaForCausalLM.__init__sU  #F+  ++yy!3!3V5F5FUS  r6c||_yr8r)r3decoders r5 set_decoderz DiffLlamaForCausalLM.set_decoders  r6c|jSr8rJrs r5 get_decoderz DiffLlamaForCausalLM.get_decoders zzr6r9rrQrr:labelsrrlogits_to_keeprrXc z|jd|||||||d| } | j} t| tr t | dn| } |j | dd| ddf}d}|*|j d|||jjd| }t||| j| j| jS)a Example: ```python >>> from transformers import AutoTokenizer, DiffLlamaForCausalLM >>> model = DiffLlamaForCausalLM.from_pretrained("google/diffllama-7b") >>> tokenizer = AutoTokenizer.from_pretrained("google/diffllama-7b") >>> prompt = "What is your favorite condiment?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "What is your favorite condiment?" ```)r9rrQrr:rrN)rGrOr/)lossrGrrVrr) rr=rrslicerE loss_functionr)r/rrrVr)r3r9rrQrr:rOrrrProutputsrV slice_indicesrGrRs r5r:zDiffLlamaForCausalLM.forwards@,64:: , )%+') ,  ,  11 8B>SV8W~ot4]k mA}a,?@A  %4%%pVFt{{OeOepiopD%#33!//))   r6) NNNNNNNNr)r;r<r=_tied_weights_keys_tp_plan_pp_planr(rLrNrrrrFrrrrBrrrrrrr:r>r?s@r5rDrDs:*+=)H_-z:;H151537+/59-1$(59348 E,,-8 !.8 u//0 8 "% 8   1 12 8 ))*8 D>8 !!1!128 c5<</08 +,8  8 8 r6rDc eZdZy)"DiffLlamaForSequenceClassificationNr;r<r=rr6r5r[r[r6r[ceZdZdZy)DiffLlamaForQuestionAnswering transformerN)r;r<r=rrr6r5r_r_s%r6r_c eZdZy)DiffLlamaForTokenClassificationNr\rr6r5rbrbr]r6rb)rr+rDr[r_rb)Nr)FrbtypingrrrFr activationsr cache_utilsrr r generationr integrationsr masking_utilsr modeling_flash_attention_utilsrrmodeling_layersrrrrmodeling_outputsrrmodeling_rope_utilsrrmodeling_utilsrprocessing_utilsrutilsrrrr utils.genericrconfiguration_diffllamar get_loggerr;rlModuler"rJrUrrr`rergrrrrrrrr+rDr[r_rb__all__rr6r5rus0 " !;;)7/i PK-&RR/4   H % 299  (6 UU\\ U# U%,, U2a)a)HQ!1Q!hQ!/Q!hY'JryyJ(J* 1 "*6*ZIII4