g. hdZddlZddlmZmZmZmZddlZddlZddlm Z ddl m Z m Z m Z mZddl mZddlmZdd lmZmZmZdd lmZdd lmZdd lmZmZmZmZm Z dd l!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-ddl.m/Z/erddl0m1Z1e+rddl2m3Z3e-j4e5Z6dZ7dZ8Gdde j9Z:dZ;dGdZ<Gdde j=Z>Gdde>Z?Gdd e>Z@d!ejAd"eBd#ejCd$ejAfd%ZDd&ejAd'ejAd(eEd)eFd$ejAf d*ZGGd+d,e j=ZHGd-d.eHZIGd/d0e j=ZJeHeHeId1ZKGd2d3e j=ZLd4ZMd5ZNGd6d7e$ZOe)d8eMGd9d:eOZPe)d;eMGd<d=eOeZQe)d>eMGd?d@eOZRe)dAeMGdBdCeOZSe)dDeMGdEdFeOZTdS)HzPyTorch Falcon model.N) TYPE_CHECKINGOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLoss LayerNormMSELoss) functional)get_activation)Cache DynamicCache StaticCache)GenerationMixin)AttentionMaskConverter))BaseModelOutputWithPastAndCrossAttentions!CausalLMOutputWithCrossAttentionsQuestionAnsweringModelOutput SequenceClassifierOutputWithPastTokenClassifierOutput)ROPE_INIT_FUNCTIONS)PreTrainedModel)"is_torch_greater_or_equal_than_2_0)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardis_flash_attn_2_available#is_flash_attn_greater_or_equal_2_10logging) FalconConfig)PretrainedConfig)_flash_attention_forwardzRocketknight1/falcon-rw-1br#c2eZdZdejdejfdZdS) FalconLinearinputreturncF||jjz}|j|S||jzSN)weightTbias)selfr( hidden_statess f/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/falcon/modeling_falcon.pyforwardzFalconLinear.forwardEs+ - 9  ty((N)__name__ __module__ __qualname__torchTensorr2r3r1r'r'Ds:)U\)el))))))r3r'c|dd|jddzf}|d|jddzdf}tj| |fdS)z*Rotates half the hidden dims of the input..Ndim)shaper7cat)xx1x2s r1 rotate_halfrDMs] 3"!'"+"" " #B 3 q "" " #B 9rc2YB ' ' ''r3c||}||}||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. ) unsqueezerD)qkcossin position_ids unsqueeze_dimq_embedk_embeds r1apply_rotary_pos_embrOUsc( -- & &C -- & &C3w;q>>C/0G3w;q>>C/0G G r3cveZdZ d deeffd ZdZejd Z xZ S) FalconRotaryEmbeddingN'?defaultconfigcXti|_|=td|||||d|_||_||_||_n_|j9|j d|j d|_nd|_|j |_|j |_||_ t|j|_ |j |j |fi|j\}|_|d|d|j|_dS) Nz`FalconRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46) rope_typefactorr>basemax_position_embeddingsrXtyperUinv_freqF persistent)super__init__ rope_kwargslogger warning_oncerXmax_seq_len_cachedoriginal_max_seq_len rope_scalinggetr[rVr rope_init_fnattention_scalingregister_bufferr]original_inv_freq) r/r>r[rZdevicescaling_factorrXrVr] __class__s r1razFalconRotaryEmbedding.__init__rsC  >   R    '(+B   D 'DN&=D #(?D % %".!'!4!8!8fFYF]F]^dFeFe!f!f!*&,&DD #(.(FD % /?+<4+>@@GG HZ[\H]_acdee ,QQQaaaZ 8 > > @ @hm %/ S%A%AekUZFZFZkk`e ^ U C C C  &,,..1F1L1L1N1NNYYZ[]^__E)UEN333C''))C''))C                D**D**vvAGv$$cff17f&;&;;;s A>EEE)NrRrSNrTrUN) r4r5r6rr#rarsr7no_gradr2 __classcell__ros@r1rQrQqs  $ )-*/*/&*/*/*/*/*/*/X@@@$U]__<<_<<<<ff*********r3rc"eZdZdZfdZxZS)&FalconDynamicNTKScalingRotaryEmbeddingznFalconRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozillacxtdd|d<tj|i|dS)Nz`FalconDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use `FalconRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to __init__).rurXrrs r1raz/FalconDynamicNTKScalingRotaryEmbedding.__init__sN    ({$)&)))))r3rrs@r1rrs>xx*********r3rattention_mask num_headsr|r)c|j\}}dtjtj|z}t jddtj|dz z z|jtj}t jdd|z|jtj }t j ||}||krt jddtjd|zdz z z|jtj} t|||z } t jddd| zzd|jtj } t j |t j | | gd}| ddz |zdddddf} |d| z} | ||zd||S) Nr<r rmr|r"rr=r;).N)r?mathfloorlog2r7tensorrmfloat32arangeint32powminr@cumsumbfloat16reshaper)rrr| batch_size seq_lengthclosest_power_of_2rZpowersslopes extra_basenum_remaining_heads extra_powers arange_tensoralibis r1build_alibi_tensorrs+1J dj9)=)=>>> < ty!344q899:;NDYafan   D\!Q!33NI111dTUTUTU:VM 9  & & ( (= 8E ==i/J ? ? B B5 I IIr3rAresidualprobtrainingc>tj|||}||z}|S)a  Dropout add function Args: x (`torch.tensor`): input tensor residual (`torch.tensor`): residual tensor prob (`float`): dropout probability training (`bool`): training mode )pr)Fdropout)rArrrouts r1 dropout_addrs( )A 1 1 1C S.C Jr3c|eZdZddeffd ZdejdeejejejffdZdejdejfdZ dd ejd e ejd ejd e ej de e de ejde de de ej de eejejffdZxZS)FalconAttentionNrVclt||_|j|_|j|_|j|jz|_|j|_|j|_|j |_ |j |_ d|_ |j dk|_ ||_|(td|jjd|j|jz|jkr t'd|jd|jddt)j|jz |_|j|_|jr|jd z|jz|jz}n$|jr|jd |jzz}n d |jz}t7|j||j |_|j|_|j|_t7|j|j|j |_t?j |j!|_!|js|js|jnd |_|j"rtG|j |_$dSdS)NTsdpazInstantiating 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.zA`hidden_size` must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).rTr<r r.r"rV)%r`rarV hidden_sizenum_attention_headsrhead_dim split_sizehidden_dropoutr[ rope_theta is_causal_attn_implementation _use_sdpa layer_idxrcrdror4 ValueErrorrsqrtinv_norm_factorbetanew_decoder_architecture num_kv_heads multi_queryr'r.query_key_valuedenserDropoutattention_dropoutrotaryrQ rotary_emb)r/rVr qkv_out_dimros r1razFalconAttention.__init__sR  !-3(DN: *$3'-'E$ +4>"     ,!8,,,    =4> )T-= = ='TXTd''N'''  #TYt}%=%==(  * /!.2V5OOSWS``KK   /*Q->>KKd..K+D,z0FalconAttention._split_heads..Ws$ N N NQ1a N N Nr3r .rr") rr?viewrrrr7 broadcast_tor) r/rbatchrq_qkvquerykeyvaluerrthree_times_hidden_sizes r1 _split_headszFalconAttention._split_headsCs  ( \ ) E7A..T^tGX5X[\5\^b^kllC111aaa" %EaaaAAAtm$C111aaa" &E$S%+66C&uek::E N N5#u:M N N N E3#u$ $! \>Go ;J $;!z:t~qRVR_``IS!QQQY'31119)=yaQRQRQR?SS S>Go ;J $;!z:t~PQ?QSWS`aaIS#2#qqq[)9S2$\+BIcTVSWYZYZYZlD[[ [r3rAc|j\}}}||jz}|||j||j}|dddd}||||j|jzS)z Merge heads together over the last dimension Args: x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim] Returns: torch.tensor: [batch_size, seq_length, num_heads * head_dim] rr<r"r )r?rrrpermuter)r/rAbatch_size_and_num_headsrrrs r1 _merge_headszFalconAttention._merge_headscsv34'/ *a-?  FF:t~z4= I I IIaAq ! !yyZ$-1OPPPr3Fr0rrrK layer_past head_mask use_cacheoutput_attentionscache_positionposition_embeddingsc 0 ||} |jr|jn|j} || \} }}| j\}}}}| dd||j||j} |dd|| ||j}|dd|| ||j}|P| 4t d| ||\}}n| \}}t| |||\} }|>d| i}|| ||d| |||j|\}}|jd}|jrN| jjdkr>|<| } |}|}||ddddddd|jdf}| |jrB|s@|jr ||dkrdnd }t(jj| |||d | }d}nY| |d dz}|t1j|jz}t5j||zd |j }||z}|||j||j}|dddd}||||j|jz}||}|r|||fS||fS|jr|s||jr ||dkrdnd }t(jj| ||||j r |j!j"nd | }|dd}||||j|jz}||}nA| |d dz}|||j||}|j}|t(j#ks|t(j$kr|%t(j&}||||jdd z}||j'z}t5j||zd |j }|!|}|||z}|||j||}||z(dd}|)|}||}|r|||fS||fS)Nr"r<YThe 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.rrJrIrcudaTF) attn_mask dropout_prr;)r>r|rr )*rrrrrr?rrrrcrdrrOupdaterrrmr\ contiguousrr7rr scaled_dot_product_attentionrrrsoftmaxr|rrrrrrfloat16rrrrrr)r/r0rrrKrrrrrrrr query_layer key_layer value_layerr query_lengthrrIrJ cache_kwargs kv_lengthr attn_outputattention_scores matmul_result input_dtypeattention_logitsattention_probsattention_probs_reshapeds r1r2zFalconAttention.forward|s((77 )-)F]t~~DL] 040A0A)0L0L-i)4):& L!Q!++Aq1199*dnVbdhdqrr ''1--55j,P\^b^kll !++Aq1199*lT`bfbopp ="*##K  ??; EESS.S%9+yRUWZ%[%[ "K  !,n=L}##C$<$<===%/%6%6y+t~_k%l%l "I{OB' > 3k05??ND^&0022K!,,..I%0022K  %+AAAqqq!!!5Jyr7J5J,JKN =~ =&7 = %)Nm~7MR^abRbRbDDhm #h1NN,!' O $(  #.1D1DR1L1L#L  DIdm$<$<< #$9-=-NTV^k^q#r#r#r .< %**:t~|UYUbccK%--aAq99K%--j,Y]YfHfggK**[11K  /"J0@@@"J..~. 6&7. 6I-Pd466S' O *33Aq99 )11*lDN]a]jLjkk "jj55 +i.A.A"b.I.I I $1#5#5j$.R^`i#j#j /4 %-//;%.3P3P'7':':5='I'I$#3ejjT^]^`b6c6c#c  D$88 "#),<~,MSU]j]p"q"q"q"&"8"8"I"I(&5 &AO,;+?+? DN\hjs+t+t( 8+ENNqRSTT #// << "jj55   /"J??"J..r3r+NNNFFNN)r4r5r6r#rar7r8rrrr LongTensorrboolr2rrs@r1rrs-H-H|-H-H-H-H-H-H^\el\uU\5d| i}|| ||d| |||j|\}}| dd} |dd}|dd}|td|jr |jjnd}| j}|t(jkrt)jrt)j}n3t1|jdr |jj}n|jjj}t d |d | |} ||}||}t9| |||||||j|j }||||j|jz}||}|sd}|||fS) Nr"r<rrrz6`alibi` is not supported when `use_flash_attn` is Truer_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 .)rKrruse_top_left_mask) rrrrrr?rrrrcrdrrOrrrrrVrr|r7ris_autocast_enabledget_autocast_gpu_dtypehasattrrr,rr%rrr)r/r0rrrKrrrrrrrrrrrrrrrIrJr attn_dropoutr  target_dtyper attn_weightss r1r2zFalconFlashAttention2.forwardsY((77 )-)F]t~~DL] 040A0A)0L0L-i)4):& L!Q!++Aq1199*dnVbdhdqrr ''1--55j,P\^b^kll !++Aq1199*lT`bfbopp ="*##K  ??; EESS.S%9+yRUWZ%[%[ "K  !,n=L}##C$<$<===%/%6%6y+t~_k%l%l "I{"++Aq11 ''1-- !++Aq11  UVV V8< Nt{443 "' %- ' '(** A$;== &?@@ A#{B #3:@   $ $$$    &..66K! \22I%..66K.     % n"A    #**:|T^VZVcEcdd jj..   LJ 44r3r)r4r5r6rrar7r8rrrrrr2rrs@r1rr s%XXXXX48&*,0"'59KO_5_5|_5 %_5 _5 u/0 _5 UO _5EL)_5_5 _5!!12_5&eEL%,,F&GH_5_5_5_5_5_5_5_5r3rcHeZdZdeffd ZdejdejfdZxZS) FalconMLPrVc$t|j}t||j|j|_t|j|_ t|j||j|_ |j |_ dS)Nr) r`rarr'ffn_hidden_sizer. dense_h_to_4hr activationact dense_4h_to_hr)r/rVrros r1razFalconMLP.__init__s ( )+v7MTZT_```!&"344)&*@+TZT_```$3r3rAr)c|||}||}|Sr+)r%r#r&)r/rAs r1r2zFalconMLP.forwards9 HHT''** + +   q ! !r3) r4r5r6r#rar7r8r2rrs@r1r r ~sj4|444444%,r3r )eagerrflash_attention_2cBeZdZddeffd Z ddejdeejdejdeejd ee e e ejejffd eejd e d e d eejdee ejejffdZ xZS)FalconDecoderLayerNrVc`t|j}|j|_t |j|||_t||_ |j |_ ||_ |j |j rd|_ |js8t||j|_t||j|_dS|j dkr8t||j|_t||j|_dSt||j|_dS)Nr<eps)r`rarrrFALCON_ATTENTION_CLASSESrself_attentionr mlprrVnum_ln_in_parallel_attnr parallel_attnr layer_norm_epsilonpost_attention_layernorminput_layernormln_attnln_mlp)r/rVrrros r1razFalconDecoderLayer.__init__s! ( 36v7RSTZ\effV$$$3  ) 1f6U 1-.F *# ],5kvG`,a,a,aD )#,[f>W#X#X#XD -22(&:STTT ' 9RSSS '0&B['\'\'\$$$r3Fr0rrrKrrrrrrc |} |jjr;|jjdkr+||} ||}n||} || |||||||| |  }|d}|jjsF|jjr| }n7t|| |jj |j } | | }|jjr|jjr|jjdkr| }|dd}| |}|jjs |jjr||z }t|| |jj |j }|r|f|z}n|f|ddz}|S)Nr<) rrrKrrrrrrr)rr")rVrr2r7r8r6r0r3rrrr5r1r)r/r0rrrKrrrrrrrrattention_layernorm_outmlp_layernorm_out attn_outputsattention_outputoutputs mlp_outputoutputs r1r2zFalconDecoderLayer.forwards! ; / JDK4W[\4\4\&*ll=&A&A # $ M : :  &*&:&:=&I&I #** #!)%/) 3+   (?{3 L{( L$;!!&$h 0MX\Xe%)$A$A($K$K! K 0 8 ) 8 3q88 7 qrr"XX/00 ; / +4;3L + * *JZ4;3MX\Xefff  .i')GGi'!""+-Gr3r+r)r4r5r6r#rar7r8rrrrrrr2rrs@r1r+r+sH]]|]]]]]]<48PT,0"'59KOEE|E %E E u/0 E U5% el0J*K#KLM EEL)EE E!!12E&eEL%,,F&GHEEEEEEEEr3r+a- This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`FalconConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. a7 Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. ceZdZdZeZdZdZdgZdZ dZ dZ dZ dZ fdZdejfdZed d ed d fd ZxZS)FalconPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. transformerTr+c:tj|i|dSr+)r`ra)r/inputsrros r1razFalconPreTrainedModel.__init__`s%&+F+++++r3modulect|tjst|trT|jjd|jj|j |j j dSdSt|tj r_|jjd|jj|j +|jj|j  dSdSt|tr?|j j |jjddSdS)zInitialize the weights.r)meanstdNrT)rrLinearr'r,datanormal_rVinitializer_ranger.zero_ Embedding padding_idxr fill_)r/rFs r1 _init_weightsz#FalconPreTrainedModel._init_weightscs8 fbi ( ( *Jv|,L,L * M  & &CT[5R & S S S{&  &&((((('&  - - * M  & &CT[5R & S S S!- "6#56<<>>>>>.-  * * * K  " " $ $ $ M  $ $S ) ) ) ) ) * *r3Fhard_check_onlyr)r$c|rtstdts|St|dd}|r|S|sd|_|S)NzQPyTorch SDPA requirements in Transformers are not met. Please install torch>=2.0.use_bettertransformerFr)r ImportErrorgetattrr)clsrVrS_is_bettertransformers r1_check_and_enable_sdpaz,FalconPreTrainedModel._check_and_enable_sdpatsg  w5 w!"uvvv1 M '-De L L  M 1*0F ' r3)F)r4r5r6rr# config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modules_supports_flash_attn_2_supports_sdpa_supports_cache_class_supports_quantized_cache_supports_static_cacherarModulerR classmethodrrZrrs@r1rBrBPs  L%&*#-.!N  $!,,,,,*BI****"TN`[r3rBz`The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.ceZdZdeffd ZdZdejfdZe e e e e e ddeejd eeeeeejejfd ffd eejd eejd eejdeejdeedeedeedeedeejdeeejd fe ffdZd ejdejdejd eded ejdejfdZed ejdededejdejdejdefdZxZS) FalconModelrVc*tj|_j|_j|_tj j |j|_ tj fdtjD|_jdk|_jdk|_t'|jj|_t-|_d|_|dS)Nc2g|]}t|S))r)r+)rirVs r1rz(FalconModel.__init__..s'qqqA 26Q G G Gqqqr3r)rr-rF)r`rar embed_dimrrr use_alibirrO vocab_sizeword_embeddings ModuleListrangenum_hidden_layershr_use_flash_attention_2rr r4ln_frQrgradient_checkpointing post_initr/rVros `r1razFalconModel.__init__s    +3 "|F,=t~NNqqqqQVW]WoQpQpqqqrr&,&AEX&X#4>dn&2KLLL /v>>>&+# r3c|jSr+rnr/s r1get_input_embeddingsz FalconModel.get_input_embeddingss ##r3new_embeddingsc||_dSr+ryr/r|s r1set_input_embeddingsz FalconModel.set_input_embeddingss-r3 checkpoint output_typer[N input_idspast_key_values.rrKr inputs_embedsrroutput_hidden_states return_dictrr)c ||n |jj}| | n |jj} ||n |jj}| | n |jj} |du|duz rt d|jr%|jr|rt dd}|| |}d} |rVt|tsAd} |t}n.tj|}t dd} ||nd}|j\}}}|jrK|+t%j|||zf|jt$jn|}t-||j|j} | t%j|||z|j } || d}|||| |||| }|||jj}|}|||}d}|rd nd}| rd nd}t?|j D]\}}| r||fz}|jr2|jr+|!|j"|| |||||||| | }n|||||||||| | | }|d}|dur|d }|r|||rd nd fz}|#|}| r||fz}|r|nd}| r|$}| stKd||||fDStM||||S)Nz:You must specify exactly one of input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...FTzWe detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class (https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)rrr{rvr9) rrrKrrrrrrr"r<c3K|]}||V dSr+r9)rvs r1 z&FalconModel.forward..;s1bcbobobobobor3)last_hidden_staterr0 attentions)'rVrrruse_return_dictrrurrcrdrnrrrfrom_legacy_cacheget_seq_lengthr?rlr7onesrmlongrrr|rrF_update_causal_mask get_head_maskrqr enumeraterr_gradient_checkpointing_func__call__rtto_legacy_cachetupler)r/rrrrKrrrrrrrreturn_legacy_cacherpast_key_values_lengthrrrmask causal_maskr0rnext_decoder_cacheall_self_attentionsall_hidden_statesrjblockr> next_caches r1r2zFalconModel.forwardsl(2C1N--TXT_Tq$8$D $+Jj "+!6IIDK X ") .D!DEmNbjojt$ 'tT^=CVWWWE  !"\&(>(KTaThN  )33A66L.. M>?L]_hjo  &&y$+2OPP % #oom\JJ!$5?bb4"6@BBD!$&))% ^% ^HAu# I$58H$H!* t} ;;N! aL#%"'   %!.#.!-'l'&7#1(;   $AJMD  %,QZ"  ^&9W)EZQQYZ=[<]&]# -00  E 1]4D D +4>''$  6#3355J ):7HJ]^ 9+&+*     r3 input_tensorrc n|jjdkr |d|vr|SdS||nd}t|t} |jjdkr'| s%|s#|!|t j||||jrdS|j|j } } tj | j } |j \} }}| r|}n,t|tjr |j dn||z}||||| | ||j d}|_|]|j| dg|j ddR}tj|t'j|jj|jzz |dk| }|jjdkr)|'|j jd kr|st j|| }|S) Nr)rrr)rr is_trainingr;)sequence_length target_lengthr|rmrrr"r)rVrrrrr_ignore_causal_mask_sdparr|rmr7finforr?get_max_cache_shaper85_prepare_4d_causal_attention_mask_with_cache_positionr masked_fillrrrrr\_unmask_unattended)r/rrrrrrrpast_seen_tokensusing_static_cacher|rm min_dtyperrrrrs r1rzFalconModel._update_causal_maskFs- ; +/B B B)c^.C.C%%4 @O?Z?99;;;`a'EE K , 6 6& 7% 7! %>*'7 M   t$*L,?vK&&* )5);& OQ  +??AAMMnel;;8$R((%7 PP +')#)!,Q    !2!EM*bC5;qrr?CCCE+ $+"9T^"KLLLb K K , 6 6*%*f44%5 1CKQZ[[Kr3rrr|rmrc ||dkr|}n+tj|j} tj||f| ||}|dkrtj|d}|tj|||ddkz}|ddddddf|ddd}|| }|j d} |ddddddd| f|ddddddfz} | dk} |ddddddd| f | | |ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. N) fill_valuer|rmr")diagonalrvr;r) r>r7rrfulltriurrr~cloner?r) rrrr|rmrrrrr mask_length padding_masks r1rzAFalconModel._prepare_4d_causal_attention_mask_with_cache_positionsD  %.*<*<*>*>!*C*C(KK E**.I* -0Ye\bK!###jqAAA 5< fEEEH^H^_acdHeHee eK%dD!!!QQQ&67>>z1bRTUUK))//11 ,226 *111aaaL[L+@ANSTSTSTVZ\`bcbcbcScDdd +q0 5@AAAqqq,;,AV5W5c5c )66 AAAqqq!!!\k\12r3) NNNNNNNNNNN)r4r5r6r#rar{r7r8rrFALCON_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr_CONFIG_FOR_DOCrrrrrrr2r staticmethodintr|rmrrrs@r1rgrgs |2$$$.5<....+*+BCC&=$15ae15370448$(,0/3&*59S S E,-S "%uU5<;U5VX[5[/\(\"]^S !. S u/0 S E,- S   01S D>S $D>S 'tnS d^S !!12S  uU\3&')RR SS S S DC S jU UlU U  U  U<U|UUUUn5 555{ 5  5  5555\55555r3rgz{The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).ceZdZdgZdeffd ZdZdejfdZ e e e e ee dd eejd eeeeeejejfd ffd eejd eejdeejdeejdeejdeedeedeedeedeejdeeejeffdZdeeejejfd fdejdeeejejfd ffdZxZS)FalconForCausalLMzlm_head.weightrVct|t||_t j|j|jd|_| dSNFr) r`rargrCrrJrrmlm_headrvrws r1razFalconForCausalLM.__init__sa    &v..y!3V5FUSSS  r3c|jSr+rrzs r1get_output_embeddingsz'FalconForCausalLM.get_output_embeddingss |r3r|c||_dSr+rr~s r1set_output_embeddingsz'FalconForCausalLM.set_output_embeddingss % r3rNrr.rrKrrlabelsrrrrrr)c @| | n |jj} ||||||||| | | |  } | d}||}d}||dddddf}|dddf}|j\}}}t }||||z||||z}| s|f| ddz}||f|zn|St||| j | j | j S)a labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` N) rrrKrrrrrrrr.r;r"losslogitsrr0r) rVrrCrrr?r rrrr0r)r/rrrrKrrrrrrrrtransformer_outputsr0 lm_logitsr shift_logits shift_labelsrrrmloss_fctr@s r1r2zFalconForCausalLM.forwards}8&1%>L!#qrr'?5577L1=1C .J J'))H8!!*z"9:FF HYHYZdgqZqHrHrD F\$7$;;F)-)9TGf$$v E0/?-;*5     r3pastbeam_idxc\fd|Dtfd|D}|S)aL This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. Output shares the same memory storage as `past`. cZi|]'}|D]"}|j|j#(Sr9)rmr)rr past_staters r1 z4FalconForCausalLM._reorder_cache..?sO   BLgq  YcJ x{{:+<==    r3c3K|]^}|dd|dj|dd|djfV_dS)rr"N) index_selectrm)rrdevice_to_beam_idxs r1rz3FalconForCausalLM._reorder_cache..Bs  1 **1.@AAU.VWW1 **1.@AAU.VWW       r3)r)r/rrreordered_pastrs ` @r1_reorder_cachez FalconForCausalLM._reorder_cache3sn    PT       #      r3) NNNNNNNNNNNN)r4r5r6_tied_weights_keysr#rarr7r8rrrrrrrrrrrrrr2rrrs@r1rrsZ ++|&EL&&&&+*+BCC&5$15ae1537,004)-$(,0/3&*59? ? E,-? "%uU5<;U5VX[5[/\(\"]^? !. ? u/0 ? EL) ?   -? &? D>? $D>? 'tn? d^? !!12?  uU\"$EE F? ? ? DC ? B% el :;S@AMRM] uU\5</0#5 6r3ra The Falcon Model transformer with a sequence classification head on top (linear layer). [`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). ceZdZdeffd Zeeeee e  dde e j de eee je jfdfde e jd e e jd e e jd e e jd e ed e ede ede edeee je ffdZxZS)FalconForSequenceClassificationrVct||j|_t||_t j|j|jd|_| dSr) r`ra num_labelsrgrCrrJrscorervrws r1raz(FalconForSequenceClassification.__init__\sk     +&v..Yv163D5QQQ  r3rNrr.rrrrrrrrr)c | | n |jj} ||||||||| |  } | d} || } ||jd}n |jd}|jj|dkrt d|jjd}n|rtj||jj ddz }||jdz}| | j }n)d}t|jjd| tj|| j |f}d}|.|jjf|jdkr d |j_nN|jdkr7|jtjks|jtj kr d |j_n d |j_|jjd krWt-}|jdkr1|||}nb|||}nU|jjd krt1}|||}n*|jjd krt3}|||}| s|f| ddz}||f|zn|St5||| j| j| j S)  labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Nrrrrrrrrrr"z=Cannot handle batch sizes > 1 if no padding token is defined.r;z will not detect padding tokens in `inputs_embeds`. Results may be unexpected if using padding tokens in conjunction with `inputs_embeds.`rv regressionsingle_label_classificationmulti_label_classificationr)rVrrCrr? pad_token_idrr7eqrargmaxrrmrcrdror4r problem_typerr|rr squeezer rrrr0r)r/rrrrrrrrrrrr0rrsequence_lengths pooled_logitsrrr@s r1r2z'FalconForSequenceClassification.forwardes4&1%] $D>] 'tn] d^]  uU\"$DD E] ] ] DC ] ] ] ] ] r3rz Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. ceZdZdeffd Zeeeee e  dde e j de eee je jfdfde e jd e e jd e e jd e e jd e ed e ede ede edeee je ffdZxZS)FalconForTokenClassificationrVct||j|_t||_t |dd|j}nt |dd|j}nd}tj ||_ tj |j |j|_ |dS)Nclassifier_dropoutrg?)r`rarrgrCrWrrrrrrJr classifierrv)r/rVrros r1raz%FalconForTokenClassification.__init__s     +&v.. 6/ 6 6 B!'!:   V-t 4 4 @!'!6  !$ z"455 )F$68IJJ r3rNrr.rrrrrrrrr)c | | n |jj} ||||||||| |  } | d} || } || } d}|V|j\}}t }|| ||z|j|||z}| s| f| ddz}||f|zn|St|| | j | j S)rNrrr<)rrr0r) rVrrCrrr?r rrrr0r)r/rrrrrrrrrrrr0rrrrrr@s r1r2z$FalconForTokenClassification.forwards<4&1%7 $D>7 'tn7 d^7  uU\"$99 :7 7 7 DC 7 7 7 7 7 r3rz The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). c<eZdZfdZee ddeejdeej deej deej deejdeejd ee d ee d ee d e e e ffd ZxZS)FalconForQuestionAnsweringct|t||_t j|jd|_|dS)Nr<) r`rargrCrrJr qa_outputsrvrws r1raz#FalconForQuestionAnswering.__init__,sY    &v..)F$6:: r3Nrrrrstart_positions end_positionsrrrr)c | | n |jj} |||||||| } | d} || } | dd\} }| d} |d}d}||t|dkr|d}t|dkr|d}| d}| d|}| d|}t|}|| |}|||}||zdz }| s| |f| ddz}||f|zn|St|| || j | j S) a start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. N)rrrrrrrr"r;r=) ignore_indexr<)r start_logits end_logitsr0r)rVrrCrsplitrrlensizeclampr rr0r)r/rrrrrrrrrr>sequence_outputrrr total_loss ignored_indexr start_lossend_lossr@s r1r2z"FalconForQuestionAnswering.forward4s.&1%D 'tnD d^D  u22 3D D D DCD D D D D r3r)Nr")Urrtypingrrrrr7torch.utils.checkpointrtorch.nnrr r r r r activationsr cache_utilsrrr generationrmodeling_attn_mask_utilsrmodeling_outputsrrrrrmodeling_rope_utilsrmodeling_utilsr pytorch_utilsrutilsrrrrr r!configuration_falconr#configuration_utilsr$modeling_flash_attention_utilsr% get_loggerr4rcrrrJr'rDrOrdrQrrr8rr|rr}rrrrr r/r+FALCON_START_DOCSTRINGrrBrgrrrrr9r3r1rsz 888888888888 LLLLLLLLLLLL$$$$$$))))));;;;;;;;;;))))))766666------??????/.....8777777KJJJJJJ  H % %3  )))))29)))(((8T<T<T<T<T * * * * * * * *-B * * *Ju|JJEKJ\a\hJJJJ:5<5<uPTY^Ye&v/v/v/v/v/biv/v/v/ro5o5o5o5o5Oo5o5o5d        " .________D GT44444O444nfIIIII'II IX Bnnnnn-nn nb m m m m m &;m m m ` O O O O O #8O O O d N N N N N !6N N N N N r3