T^(h`dZddlZddlZddlZddlZddlmZmZmZm Z ddl Z ddl m Z ddl m Z mZmZddlmZddlmZmZmZmZdd lmZdd lmZdd lmZmZmZmZm Z m!Z!m"Z"dd l#m$Z$dd l%m&Z&m'Z'ddl(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4ddl5m6Z6e-r ddl7m8Z8ddl9m:Z:e0jve<Z=dZ>dZ?dZ@dZAGdde jZCGdde jZDGdde jZEGdde jZFGd d!e jZGGd"d#e jZHGd$d%e jZIGd&d'e jZJd(ZKGd)d*e jZLGd+d,e$ZMGd-d.eMZNd/ZOd0ZPd1ZQd2ZRe+d3eOGd4d5eMZSe+d6eOGd7d8eMeZTe+d9eOGd:d;eMZUe+deMZVe+d?eOGd@dAeMZWe+dBeOGdCdDeMZXgdEZYy)FzPyTorch mT5 model.N)ListOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)Cache DynamicCacheEncoderDecoderCache StaticCache)GenerationMixin)AttentionMaskConverter)BaseModelOutput)BaseModelOutputWithPastAndCrossAttentionsSeq2SeqLMOutputSeq2SeqModelOutput#Seq2SeqQuestionAnsweringModelOutputSeq2SeqSequenceClassifierOutputTokenClassifierOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) DUMMY_INPUTS DUMMY_MASKadd_start_docstrings%add_start_docstrings_to_model_forwardis_torch_flex_attn_availableis_torch_fx_proxyis_torchdynamo_compilingloggingreplace_return_docstrings)assert_device_mapget_device_map) MT5Config) BlockMask)make_flex_block_causal_maskr)z mt5-smalla This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, *optional*): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the mt5 models have the following number of attention modules: - mt5-small: 6 - mt5-base: 12 - mt5-large: 24 - mt5-xl: 24 - mt5-xxl: 24 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using mt5-xl, which has a total of 24 attention modules: model = MT5ForConditionalGeneration.from_pretrained("mt5-xl") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) ``` a# Moves the model to cpu from a model parallel state. Example: ```python # On a 4 GPU machine with mt5-xl: model = MT5ForConditionalGeneration.from_pretrained("Mt5-xl") device_map = { 0: [0, 1, 2], 1: [3, 4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15, 16], 3: [17, 18, 19, 20, 21, 22, 23], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` c&eZdZdfd ZdZxZS) MT5LayerNormct|tjt j ||_||_y)zd Construct a layernorm module in the MT5 style. No bias and no subtraction of mean. N)super__init__r Parametertorchonesweightvariance_epsilon)self hidden_sizeeps __class__s b/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/mt5/modeling_mt5.pyr0zMT5LayerNorm.__init__s1 ll5::k#:; #c|jtjjdj dd}|tj ||j zz}|jjtjtjfvr%|j|jj}|j|zS)NT)keepdim) tor2float32powmeanrsqrtr5r4dtypefloat16bfloat16)r6 hidden_statesvariances r:forwardzMT5LayerNorm.forwards !##EMM266q9>>r4>P% Ht?T?T4T(UU  ;;   ? ?),,T[[->->?M{{]**r;)gư>)__name__ __module__ __qualname__r0rJ __classcell__r9s@r:r-r-s $ +r;r-c*eZdZdeffd ZdZxZS)MT5DenseActDenseconfigc^t|tj|j|j d|_tj|j |jd|_tj|j|_ t|j|_ yNFbias)r/r0rLineard_modeld_ffwiwoDropout dropout_ratedropoutr dense_act_fnactr6rRr9s r:r0zMT5DenseActDense.__init__sn ))FNNFKKeD))FKKeDzz&"5"56 &--.r;c|j|}|j|}|j|}t|jj t jr|j|jj jk7r`|jj jt jk7r/|j|jj j}|j |}|SN) rZr`r^ isinstancer[r4r2TensorrEint8r@r6rHs r:rJzMT5DenseActDense.forwards . /  ]3 tww~~u|| 4##tww~~';';;$$ 2),,TWW^^-A-ABM . r;rKrLrMr)r0rJrNrOs@r:rQrQs/y/ r;rQc*eZdZdeffd ZdZxZS)MT5DenseGatedActDenserRct|tj|j|j d|_tj|j|j d|_tj|j |jd|_tj|j|_ t|j|_yrT)r/r0rrWrXrYwi_0wi_1r[r\r]r^r r_r`ras r:r0zMT5DenseGatedActDense.__init__s IIfnnfkkF IIfnnfkkF ))FKKeDzz&"5"56 &--.r;c,|j|j|}|j|}||z}|j|}t |j j tjr|j|j j jk7r`|j j jtjk7r/|j|j j j}|j |}|Src) r`rlrmr^rdr[r4r2rerErfr@)r6rH hidden_gelu hidden_linears r:rJzMT5DenseGatedActDense.forwardshhtyy78  -0 #m3  ]3 tww~~u|| 4##tww~~';';;$$ 2),,TWW^^-A-ABM . r;rhrOs@r:rjrjs/y/r;rjc*eZdZdeffd ZdZxZS) MT5LayerFFrRct||jrt||_nt ||_t |j|j|_ tj|j|_ y)Nr8)r/r0 is_gated_actrjDenseReluDenserQr-rXlayer_norm_epsilon layer_normrr\r]r^ras r:r0zMT5LayerFF.__init__s_    "7"?D "26":D &v~~6;T;TUzz&"5"56 r;cr|j|}|j|}||j|z}|Src)rxrvr^)r6rHforwarded_statess r:rJzMT5LayerFF.forwards=??=9../?@% 5E(FF r;rhrOs@r:rrrrs7y7r;rrcneZdZ ddedeeffd ZdZed dZ d dZ d dZ xZ S) MT5AttentionrR layer_idxct||j|_||_|j|_|j |_|j |_|j|_|j|_ |j|_ |j|jz|_ ||_|9|jr-tj!d|j"j$dt'j(|j |jd|_t'j(|j |jd|_t'j(|j |jd|_t'j(|j|j d|_|jr/t'j2|j|j|_t7|_d|_y)NzInstantiating a decoder z without passing `layer_idx` is not recommended and will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` when creating this class.FrU)r/r0 is_decoderhas_relative_attention_biasrelative_attention_num_bucketsrelative_attention_max_distancerXd_kvkey_value_proj_dim num_headsn_headsr]r^ inner_dimr}logger warning_oncer9rKrrWqkvo Embeddingrelative_attention_biasset pruned_headsgradient_checkpointingr6rRrr}r9s r:r0zMT5Attention.__init__ss  +++F(.4.S.S+/5/U/U,~~ "(++'' ** (?(??"     *4>>+B+B*CD,, 4<<eD4<<eD4<<eD4>>4<=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) rr=r() r@r2longabsmin zeros_likelogfloatmath full_likewhere)relative_position bidirectional num_buckets max_distancerelative_buckets max_exactis_smallrelative_position_if_larges r:_relative_position_bucketz&MT5Attention._relative_position_buckets(,  A K !2Q!6 : :5:: F T T  % *; < !&+N>NO`>a!b b  1$ $y0&/ II'--/); <hh|i/0 1Y& ( "UZZ. &" &+YY &8RT_bcTc(d& " EKK2CE_``r;c | |jjj}|.tj|tj |dddf}n|dddfj |}tj|tj |dddf}||z }|j||j |j|j}|j|} | jgdjd} | S)z%Compute binned relative position biasN)rEdevice)rrr)r=rr(r) rr4rr2arangerr@rrrrpermute unsqueeze) r6 query_length key_lengthrcache_positioncontext_positionmemory_positionrrelative_position_bucketvaluess r: compute_biaszMT5Attention.compute_biasEs >1188??F  !$||L SYZ[\^b[bc -ag699&A ,,zFSTXZ[T[\+.>>#'#A#A #.;;== $B$  --.FG *44Q7 r;c |jdd\} } |du} |j|}|j| d|j|jj dd}|@|j j|j}| r |j}n |j}| r|n|}| r7|5r3j|j}|j|j}n|j|}|j|}|j| d|j|jj dd}|j| d|j|jj dd}|D| s| nd} j|||jd| i\}}| rd|j |j<t!j"||j dd}||jd}||n| ddz}|j$sZt!j&d|j| |f|j(|j* }|j,rE|j.r9d|_n1|j3|||j(| }|dddd| dddf}|#|ddddddd|jdf}||z}|j4rRt!j6|jd}d |t9|j4<|dd|j;f}n|}||z }t<j>jA|jCd jE|}t<j>jG||jF|j. }|||z}t!j"||}|j ddjI}|j| d|jJ}|jM|}|||f}| r||fz}|S)z Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). 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K'~~c"     KK)CHHTN#34 5 NN8T *  tBx  KK)CHHTN#34 5 NN8T * 8$* 'F||,f5 hhy&9 %h 1~!AA!'84Q#33!'73!!*Q#66!'73!!*Q#55!'73!!*Q:-7L1%g|G;1$+a.)!#,U* 'V q>!A Agx0G q>[ ( KK zzF$=$=>  &..&2C2CD r;rHreturnc|j|}|j|}tj|}|j|}|j |}|Src)r^rcr2tanhrfrgs r:rJzMT5ClassificationHead.forwardsN ]3  =1  =1  ]3  m4 r;) rKrLrM__doc__r)r0r2rerJrNrOs@r:raras/7EyE U\\ellr;racXeZdZdZeZeZdZdZ dZ dZ dZ dZ dgZdgZedZdZd Zy ) MT5PreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. transformerTFrr[cvtjt}tjt}|||d}|S)N)decoder_input_ids input_idsdecoder_attention_mask)r2tensorrr)r6rp input_mask dummy_inputss r:rtzMT5PreTrainedModel.dummy_inputss6LL. \\*- !*"&0 r;cx|jj}t|tr)|jj j |dzy t|ttttfr|jjj jd|dzt|drJ|jjs4|jjj jd|dzt|drz|j jj jd||jj"dzz|j j$j j'y y t|t(rpt|drc|j*jj jd|dz|j*j$j j'y y t|t,rM|j.jj jd||jj"dzzt|j.drD|j.j$.|j.j$j j'|j0jj jd||jj"dzzt|j0drF|j0j$/|j0j$j j'y y y t|t2rM|j4jj jd||jj"dzzt|j4drD|j4j$.|j4j$j j'|j6jj jd||jj8dzzt|j6drF|j6j$/|j6j$j j'y y y t|t:r|j<jj jd||jj"dzzt|j<drD|j<j$.|j<j$j j'|j>jj jd||jj"dzzt|j>drD|j>j$.|j>j$j j'|j6jj jd||jj8dzzt|j6drF|j6j$/|j6j$j j'y y y t|t@ri|jj"}|jjB}|jjD}|jFjj jd|||zdzz|jHjj jd||dzz|jJjj jd||dzz|jLjj jd|||zdzz|jNr8|jPjj jd||dzzy y y ) zInitialize the weights?)rCstdr1 qa_outputsr.rVN))rRinitializer_factorrdr-r4rMfill_MT5ModelMT5ForConditionalGenerationMT5EncoderModelMT5ForQuestionAnsweringsharednormal_rFtie_word_embeddingsr1ryrXrVzero_MT5ForTokenClassificationr.rarcrfrQrZr[rYrjrlrmr|rrrrrrrr)r6modulefactorrXrrs r: _init_weightsz MT5PreTrainedModel._init_weightss// fl + MM   $ $Vc\ 2   2OE\ ] MM % % - -3FSL - Ivy)$++2Q2Q%%**22#2Nv|,!!((--553Ft{{ObObgkNkDl5m!!&&++113- 9 :v|,!!((--553FSL5Q!!&&++113- 5 6 LL   $ $ , ,#6dkkFYFY^bEb;c , dv||V,1B1B1N !!&&,,. OO " " ' ' / /SfI\I\aeHe>f / gv/FOO4H4H4T$$))//15U/  0 1 II   ! ! ) )s4;;CVCV[_B_8` ) avyy&)fiinn.H ##))+ II   ! ! ) )s4;;CSCSX\B\8] ) ^vyy&)fiinn.H ##))+/I)  5 6 KK   # # + +&T[[EXEX]aDa:b + cv{{F+ 0@0@0L   %%++- KK   # # + +&T[[EXEX]aDa:b + cv{{F+ 0@0@0L   %%++- II   ! ! ) )s4;;CSCSX\B\8] ) ^vyy&)fiinn.H ##))+/I)  -kk))G!%!1!1 kk++G HHOO ( (cv'L^B^cgAg7h ( i HHOO ( (cv$7O ( P HHOO ( (cv$7O ( P HHOO ( (cv'L^B^cgAg7h ( i11..55::BBQW\chl[lQmBn2.r;c|jj}|jj}| tdt |rGt j |jdddz|}t j||dddfgd}n>|j|j}|dddfj|dddf<||d<| td|j|d k(||S) Nzself.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id. See MT5 docs for more information.r>)r(.rr().rz1self.model.config.pad_token_id has to be defined.) rRdecoder_start_token_id pad_token_id ValueErrorr"r2fullrcat new_zerosclone masked_fill_)r6rprrshifted_input_idss r: _shift_rightzMT5PreTrainedModel._shift_rightMs!%!C!C{{// ! )5  Y ' % 9??3B+?$+FH^ _  % +Q*RXZ [  ) 3 3IOO D )238)<)B)B)D c12g &(> f %  PQ Q&&'8D'@,O  r;N)rKrLrMrjr) config_classr_load_tf_weightsbase_model_prefixis_parallelizablesupports_gradient_checkpointing_supports_quantized_cache_supports_static_cache_supports_cache_class_no_split_modules_keep_in_fp32_modulespropertyrtrrrr;r:rlrlse L,O%&*# %! # !F ;oz!r;rlcbeZdZdfd ZeeddZeedZdZ dZ ddZ dde jde jd e jd ed ef d Zede jd edede j&de j(d e jdefdZxZS)MT5Stackc t||||_|j|_t j t |jDcgc]}t|t|dk(|c}|_ t|j|j|_t j|j |_|j%d|_d|_d|_ycc}w)NrrrtF)r/r0 embed_tokensrrrrange num_layersrrblockr-rXrwr*r\r]r^ post_initmodel_parallel device_mapr)r6rRrir9s r:r0zMT5Stack.__init__ks  ( ++]]^cdjdudu^v wYZXf$qAv,RS T w  !-V^^AZAZ [zz&"5"56  #&+# xs!C,ctjdt|Dtt |j t tjjn||_ t|jt |j d|_ d|jjvrdn.dtt|jjz|_dtt#|jjz|_|jj'D]E\}}|D];}dt|z}|j |j)||j |<=G|j*j)|j |_|j,j)|j$|_y)Na=`MT5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0, 'block.1': 1, ...}Tcpucuda:)warningswarn FutureWarningr'rrrr2cuda device_countrr&rrNstrr first_devicer last_deviceitemsr@rr*)r6rrrr cuda_devices r: parallelizezMT5Stack.parallelize~su  "   R\QcN3tzz?E%**2I2I2K,L Mis  $//3tzz?;"%*doo.B.B.D%DE'TWX[\`\k\k\p\p\rXsTtJt"ST__-A-A-C)D%EEOO))+ FDAq F%A. $(JJu$5$8$8$E 5! 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Setting `use_cache=False`...Fz.s = r!)last_hidden_statepast_key_valuesrH attentionscross_attentions)4rr2r set_devicerrr@rRrroutput_hidden_statesuse_return_dictrrsizerrrrrrdr rrfrom_legacy_cacheget_seq_lengthrrr#r3_update_causal_maskrrEr rrinvert_attention_mask get_head_maskrr^ enumerater_gradient_checkpointing_funcrJrrrrr*to_legacy_cachetupler)+r6rprr r r head_maskcross_attn_head_maskrrrrrrerr_msg_prefix input_shaperrreturn_legacy_cachereturn_self_attention_cachepast_key_values_lengthmask_seq_lengthrencoder_batch_sizeencoder_sequence_length_encoder_hidden_shapeencoder_extended_attention_maskall_hidden_statesall_attentionsall_cross_attentionsrr rHr layer_modulerr layer_outputsnext_decoder_cacherr next_caches+ r:rJzMT5Stack.forwards"    JJ ! !$"3"3 4 $ 1 1 4 4T5F5F GD !*!6IDKK+/??ZN*>*:.HXXvw  "#..*K!r;r?;I  &',,.s3K+/??ZN:>:J-XfWggtuv v  & &4==##p"    ( !_`` --i8M!, J  ?? #LTFRg!hii$&+# ?? _-H/51*_Vi:j.2+"5o|~"V1DE&*###` #6"G"G"X ("5lnln"U#OETE`!?!?!Afg  !"\\&(>(KTaThThN  !*B*D4zAO"ZZ OML`L`aN ;; ! !228G8S44Y]! 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"!-!#."/*?+J2O$3/I#2'&7 +#1! $E! -bq 1G ;mAB>O O 0=bq0A -M- *!,M#8#D0=CTaZ[0\- !/=3C2E!E??+?=QRCSBU+U("" OO113ODAqAbEzgA&6$:J:J&J(5(8(83q1u:9M(N O_Q Of--m<  ]3  1]4D D +4'$ &(==J (88:J "%"(    9+&+%1   r;r input_tensorrrrc |jjdk(r||dk(jr|Sy|jjdk(r7t|tj r t |}t|tr|S||jnd}t|t}|jjdk(r(|s&|s$tj||||jry|j|j} }|jd} |r|j!} n1t|tj r|jdn|| zdz} |j#|| | || ||jd } |jjdk(rQ|O|jj$d vr7|s5t j&|j(} tj*| | } | S) Nflash_attention_2rwflex_attentionrsdpa)rr is_trainingr(r>)sequence_length target_lengthrErrr)rxpu)rR_attn_implementationrrdr2rer+r*rrr_ignore_causal_mask_sdparrErrget_max_cache_shape5_prepare_4d_causal_attention_mask_with_cache_positiontyper r_unmask_unattended)r6rrrrrpast_seen_tokensusing_static_cacherErrrr min_dtypes r:rzMT5Stack._update_causal_masks ;; + +/B B)~/D.I.I.K%% ;; + +/? ?.%,,7!<^!L.)4%% @O?Z?99;`a'E ;; + +v 5>PYj%>>*'7 MM  $**L,?,?v&,,Q/ +??AMnell;$$R(%7!; PP +')#))!,Q  KK , , 6*%%**o=%  E*..I0CCKQZ[Kr;rrrErrc ||jdk(r|}|Stj|j} tj||f| ||}|dk7rtj |d}|tj |||jddkDz}|ddddddfj|ddd}||j}|jd} |ddddddd| f|ddddddfj|jz} | dk(} |ddddddd| fj| | |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 place 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. Nr) fill_valuerErr()diagonalrr>r)rr2r rrtriurreshapeexpandrrr@r masked_fill) rrrrErrrkwargsrr mask_length padding_masks r:rz>MT5Stack._prepare_4d_causal_attention_mask_with_cache_positionsyD  %.*<*<*>!*C(K*' E*..I** -0Ye\bK!##jjqA 5<< fEH^H^_acdHee eK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDg&&E   ,q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r;rc) NNNNNNNNNNNNN)F)rKrLrMr0rPARALLELIZE_DOCSTRINGrDEPARALLELIZE_DOCSTRINGrrrrJr2rer rrrrrErrrNrOs@r:rrjs+,&/0K1K612 !3 !!+ "#!!u |#( D DllD D  D  DL7 777{{ 7  7  777r;ra The MT5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting. 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, pruning heads 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 ([`MT5Config`]): 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. ab Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training). 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) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5 Training](./mt5#training). decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. 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 [`~utils.ModelOutput`] instead of a plain tuple. a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training). 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) 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. 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 [`~utils.ModelOutput`] instead of a plain tuple. a_ The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, num_heads)`. z]The bare MT5 Model transformer outputting raw hidden-states without any specific head on top.c'eZdZdZdZeZdgZddgZdeffd Z e e d"dZ e e d Zd Zd Zd Zd ZdZeeeee d#deej6deej8deej6deej:deej8deej8deej<deeeej8deeeej8deej<deej<dee dee dee dee deej6d e!eej8eff"d!Z"xZ#S)$r}aw Examples: ```python >>> from transformers import MT5Model, AutoTokenizer >>> model = MT5Model.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, return_tensors="pt") >>> labels = tokenizer(text_target=summary, return_tensors="pt") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```mt5Fdecoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weightencoder.embed_tokens.weightdecoder.embed_tokens.weightrRct||tj|j|j |_tj|}d|_ d|_ d|_ t||j |_ tj|}d|_ d|_ |j|_t||j |_|j#d|_d|_yNFT)r/r0rr vocab_sizerXrcopydeepcopyrris_encoder_decoderrencodernum_decoder_layersrr/rrrr6rRencoder_configdecoder_configr9s r:r0zMT5Model.__init__s  ll6#4#4fnnE v.$)!#( ,1) < v.$(!,1)$*$=$=! <  $r;ctjdt|Ntt |j j ttjjn||_ t|jt |j j |j j|j|jj|jd|_y)NaL`T5Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0': 0, 'encoder.block.1': 1, ...}T)rrrr'rrrrr2rrrr&rr/rr6rs r:rzMT5Model.parallelizes   -   ! 3t||112E%**:Q:Q:S4T U  $//3t||/A/A+BC   1   1"r;cxtjdt|jj |j j |jj d|_|j j d|_d|_d|_tjjyNrrF) rrrrrr/r@rrr2rrrs r:rzMT5Model.deparallelizes  j   ""$ ""$||u- ||u- #  r;c|jSrcrrs r:rzMT5Model.get_input_embeddings! {{r;c~||_|jj||jj|yrcrrrr/rs r:rzMT5Model.set_input_embeddings%-$  )).9 )).9r;c|jSrcrrs r: get_encoderzMT5Model.get_encoder+ ||r;c|jSrcr/rs r: get_decoderzMT5Model.get_decoder/r-r;c|jD]7\}}|jj|jj |9y) Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)rrr attentionrr6heads_to_prunerrs r: _prune_headszMT5Model._prune_heads3sE +002 CLE5 LL  u % / / ; ;E B Cr; output_typerrprrorqrdecoder_head_maskrencoder_outputsrrdecoder_inputs_embedsrrrrrrgc| | n|jj} ||n|jj}|O|M|jj|jjk(r t j tt|}||j||| || ||}nI|rGt|ts7t|dt|dkDr|dndt|dkDr|dnd}|d}|jrtjj!|j"j$|j'|j"j$}|%|j'|j"j$}|%|j'|j"j$}|%|j'|j"j$}|j#||| | ||||| | ||| }|s||zSt)|j*|j,|j.|j0|j2|j*|j.|j0S) a Returns: Example: ```python >>> from transformers import AutoTokenizer, MT5Model >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> model = MT5Model.from_pretrained("google/mt5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MT5Model. >>> # This is not needed for torch's MT5ForConditionalGeneration as it does this internally using labels arg. >>> decoder_input_ids = model._shift_right(decoder_input_ids) >>> # forward pass >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state ```Nrprrrrrrrr(r=rrHr rprrrr r rrrrrrr)rrdecoder_hidden_statesdecoder_attentionsrencoder_last_hidden_stater encoder_attentions)rRrrrrrr _MT5Model__HEAD_MASK_WARNING_MSGrrrdrrrr2rrr/rr@rrrrHrr)r6rprrorqrr9rr:rrr;rrrrrrHdecoder_outputss r:rJzMT5Model.forward;s=\"+!6IDKK{{%%)G)GG 5}E$-!  ""ll#-+#"3%9'+OO_!M-"1!"4474H14Loa0RV14_1E1I?1-tO (*     JJ ! !$,,";"; <),,T\\-F-FGM ,$5$8$89R9R$S!)!/!2!24<<3L3L!M%1)?)B)B4<p $D>p 'tnp d^!p "!!1!12#p $ uU&&');; <%p ]Ap r;r}z1MT5 Model with a `language modeling` head on top.c) eZdZdZdZeZdgZgdZdeffd Z e e d%dZ e e dZd Zd Zd Zd Zd ZdZeeeee d&deej8deej:deej8deej<deej:deej:deej>dee e ej>dee e ej>deej:deej:deej8dee!dee!dee!dee!d eej8d!e"e ej:eff$d"Z#dej>fd#Z$d$Z%xZ&S)'r~a Examples: ```python >>> from transformers import MT5ForConditionalGeneration, AutoTokenizer >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt") >>> outputs = model(**inputs) >>> loss = outputs.loss ```rr)rrzlm_head.weightrRcjt|||j|_t j |j |j|_tj|}d|_ d|_ d|_ t||j|_tj|}d|_ d|_ |j|_t||j|_t j$|j|j d|_|j)d|_d|_y)NFTrU)r/r0rX model_dimrrrrrrrrrrrrrr/rWr1rrrrs r:r0z$MT5ForConditionalGeneration.__init__s  ll6#4#4fnnE v.$)!#( ,1) < v.$(!,1)$*$=$=! < yy1B1BO  $r;cVtjdt|Ntt |j j ttjjn||_ t|jt |j j |j j|j|jj|j|jj!|jj"|_d|_y)Na_`T5ForConditionalGeneration.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0': 0, 'encoder.block.1': 1, ...}T)rrrr'rrrrr2rrrr&rr/r1r@rrr!s r:rz'MT5ForConditionalGeneration.parallelizes   A   ! 3t||112E%**:Q:Q:S4T U  $//3t||/A/A+BC   1   1||t||'@'@A "r;ctjdt|jj |j j |jj d|_|j j d|_|jj d|_d|_d|_ tjjyr#) rrrrrr/r@r1rrr2rrrs r:rz)MT5ForConditionalGeneration.deparallelizes  j   ""$ ""$||u- ||u- ||u- #  r;c|jSrcr%rs r:rz0MT5ForConditionalGeneration.get_input_embeddings r&r;c~||_|jj||jj|yrcr(rs r:rz0MT5ForConditionalGeneration.set_input_embeddingsr)r;c||_yrcr1rs r:set_output_embeddingsz1MT5ForConditionalGeneration.set_output_embeddingss % r;c|jSrcrWrs r:get_output_embeddingsz1MT5ForConditionalGeneration.get_output_embeddingsr-r;c|jSrcr+rs r:r,z'MT5ForConditionalGeneration.get_encoderr-r;c|jSrcr/rs r:r0z'MT5ForConditionalGeneration.get_decoder r-r;r7rprrorqrr9rr:rrr;labelsrrrrrrgc| | n|jj} ||n|jj}|O|M|jj|jjk(r t j tt|}||j||| ||||}nI|rGt|ts7t|dt|dkDr|dndt|dkDr|dnd}|d}|jr3tjj!|j"j$| || |j'| }|jrtjj!|j"j$|j)|j"j$}|%|j)|j"j$}|%|j)|j"j$}|%|j)|j"j$}|j#||| | ||||| |||| }|d}|jrtjj!|jj$|j*j)|jj$|_|j)|j*j,j.}|jj0r||j2dzz}|j+|}d}| ^t5d }| j)|j.} ||j7d |j9d | j7d }|s|f|ddz|z}||f|zS|St;|||j<|j>|j@|jB|jD|j>|j@ S) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import AutoTokenizer, MT5ForConditionalGeneration >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> # training >>> input_ids = tokenizer("The walks in park", return_tensors="pt").input_ids >>> labels = tokenizer(" cute dog the ", return_tensors="pt").input_ids >>> outputs = model(input_ids=input_ids, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits >>> # inference >>> input_ids = tokenizer( ... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model.generate(input_ids) >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True)) >>> # studies have shown that owning a dog is good for you. ```Nr=rr(r=r>r?rzr ignore_indexr> lossr0rr@rArrBr rC)#rRrrrrrr3_MT5ForConditionalGeneration__HEAD_MASK_WARNING_MSGrrrdrrrr2rrr/rrr@r1r4rrrQr rrrrrHrrr)r6rprrorqrr9rr:rrr;r]rrrrrrHrEsequence_output lm_logitsrbloss_fctoutputs r:rJz#MT5ForConditionalGeneration.forward#sl"+!6IDKK{{%%)G)GG 5}E$-!  ""ll#-+#"3%9'+OO_!M-"1!"4474H14Loa0RV14_1E1I?1-tO (*    JJ ! !$,,";"; <  "3";@U@] $ 1 1& 9     JJ ! !$,,";"; <),,T\\-F-FGM ,$5$8$89R9R$S!)!/!2!24<<3L3L!M%1)?)B)B4<\%\ "' ](&%r;rc)NNNNNNNNNNNNNNNNN)'rKrLrMrjrGr)rrHrIr0rrrrrrrrXrZr,r0r rJr%rrKrr2rLrMrNrerrrrJrirrrNrOs@r:r~r~s JL*r)s&iy6/0#1#&12 !3 !: &++?@?Y156:8<=A159=7;@D@D59=A-1$(,0/3&*59%Y E,,-Y !!2!23Y $E$4$45 Y !))9)9 : Y E--. Y $E$5$56Y 'u||4Y "%ell(;"<=Y "%ell(;"<=Y   1 12Y  ((9(9:Y ))*Y D>Y $D>Y 'tn!Y "d^#Y $!!1!12%Y & uU&&'8 9'Y ZAY x)ELL)&r;r~zgThe bare MT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.ceZdZdZdZeZdgZdeffd Ze e ddZ e e dZ dZd Zd Zd Zeeeee  dd eej2deej4deej4deej4deedeedeedeeej4effdZxZS)ra Examples: ```python >>> from transformers import MT5EncoderModel, AutoTokenizer >>> model = MT5EncoderModel.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> input_ids = tokenizer(article, return_tensors="pt").input_ids >>> outputs = model(input_ids) >>> hidden_state = outputs.last_hidden_state ```rrrRc:t||tj|j|j |_tj|}d|_ d|_ t||j |_ |jd|_d|_yNF)r/r0rrrrXrrrrrrrrrr)r6rRrr9s r:r0zMT5EncoderModel.__init__sy  ll6#4#4fnnE v.#( ,1) <  $r;ctjdt|Ntt |j j ttjjn||_ t|jt |j j |j j|jd|_y)NaC`T5EncoderModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0, 'block.1': 1, ...}T)rrrr'rrrrr2rrrr&rrr!s r:rzMT5EncoderModel.parallelize s   "   ! 3t||112E%**:Q:Q:S4T U  $//3t||/A/A+BC   1"r;ctjdt|jj |jj d|_d|_d|_tjjyr#) rrrrrr@rrr2rrrs r:rzMT5EncoderModel.deparallelize s[  j   ""$||u- #  r;c|jSrcr%rs r:rz$MT5EncoderModel.get_input_embeddings.r&r;cH||_|jj|yrc)rrrrs r:rz$MT5EncoderModel.set_input_embeddings2s$  )).9r;c|jSrcr+rs r:r,zMT5EncoderModel.get_encoder7r-r;c|jD]D\}}|jj|jdjj |Fy)r2rN)rrrrrrr4s r:r6zMT5EncoderModel._prune_heads;sP +002 PLE5 LL  u % + +A . < < H H O Pr;r7rprrrrrrrgc j||n|jj}|j|||||||}|S)a1 Returns: Example: ```python >>> from transformers import AutoTokenizer, MT5EncoderModel >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> model = MT5EncoderModel.from_pretrained("google/mt5-small") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids) >>> last_hidden_states = outputs.last_hidden_state ```r=)rRrr) r6rprrrrrrr:s r:rJzMT5EncoderModel.forwardCsJ:&1% &'tn&d^& uU&&'8 9&ZI&r;rz MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c%eZdZdgZddgZdeffd Zeee e e  dde e jde e jd e e jd e e jd e e jd e e jd e e jde ee j"de e j"de e j"de e jde ede ede ede edeee ff dZxZS)MT5ForSequenceClassificationrrrrRct||t||_t ||_|j d|_yru)r/r0r}rmraclassification_headrrras r:r0z%MT5ForSequenceClassification.__init__{s>  #F+#8#@  #r;r7rprrorqrr9rr:rr;r]rrrrrgc||n|jj}| d} |$| "td|jj| | | t d|j |}|j||||||||| | | | ||}|d}|j|jjj|j}ttj|jddkDr t d|j \}}}||ddfj#|d |ddd ddf}|j%|}d}| | j|j} |jj&|jj(dk(rd |j_nv|jj(dkDrL| j*tj,k(s| j*tj.k(rd |j_nd |j_|jj&d k(rSt1}|jj(dk(r&||j3| j3}n||| }n|jj&d k(rGt5}||j#d |jj(| j#d }n,|jj&d k(rt7}||| }|s|f|ddz}||f|zS|St9|||j:|j<|j>|j@|jB|jD|jF S)aD labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: NFz8Passing input embeddings is currently not supported for If no `decoder_input_ids` or `decoder_inputs_embeds` are passed, `input_ids` cannot be `None`. Please pass either `input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`.) rrorqrr9rr:rr;rrrrrr(z7All examples must have the same number of tokens.r> regressionsingle_label_classificationmulti_label_classificationra)$rRrNotImplementedErrorr9rKrrrmeq eos_token_idr@rrr2unique_consecutivesumrrr problem_typererErrr squeezer rrrr@rArrBr rC)r6rprrorqrr9rr:rr;r]rrrrrrdeos_maskrrr7sentence_representationr0rbrfrgs r:rJz$MT5ForSequenceClassification.forwardsQ6&1%>KbKbJcd   $)>)F  U !% 1 1) < "" )/#9/!5+'"7/!5##  "!*<< 8 89<<_=S=ST u'' Q8 9A =VW W%4%:%:" A{"1(A+">"C"CJPRT_"`abdfhiai"j))*AB  YYv}}-F{{''/;;))Q./;DKK,[[++a/V\\UZZ5OSYS_S_chclclSl/LDKK,/KDKK,{{''<7"9;;))Q.#FNN$4fnn6FGD#FF3D))-JJ+- B 0F0F GUWY))-II,./Y,F)-)9TGf$ Ev E.#33")"?"?&99$55&-&G&G")"?"?&99  r;)NNNNNNNNNNNNNNN)rKrLrMrHrIr)r0r rJr%rrKrr2rLrerrMrrrrJrNrOs@r:rros+s)s&79VW$y$++?@+JYhi15158<=A,0487;=A59=A-1$(,0/3&*!k E,,-k !.k $E$4$45 k !))9)9 : k ELL) k $ELL1k 'u||4k "$u'8'8"9:k   1 12k  ((9(9:k ))*k D>k $D>k 'tnk d^!k " u55 6#k jAk r;rz MT5 Encoder 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. cPeZdZdgZdeffd Zeeee e  dde e jde e jde e jde e jd e e jd e ed e ed e ed eee je ffdZxZS)rz'transformer.encoder.embed_tokens.weightrRc,t|||j|_t||_t j |j|_t j|j|j|_ |jyrc) r/r0rerrmrr\rdr^rWr7r.rras r:r0z"MT5ForTokenClassification.__init__ sj   ++*62zz&";";< ))F$6$68I8IJ r;r7rprrrr]rrrrgc ||n|jj}|j|||||||} | d} |j| } |j | } d} |r=)rbr0rHr) rRrrmr^r.r rrerrHr)r6rprrrr]rrrrrHr0rbrfrgs r:rJz!MT5ForTokenClassification.forward s&&1%. 'tn. d^.  uU\\"$99 :. `A. r;rz MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). c'heZdZdgZddgZdeffd ZdZdZdZ d Z e e e ee  dd eej$d eej&d eej$deej(deej&deej&deej*deeeej*deej$deej$deej&deej&deedeedeedeedeeej&eff"dZxZS)rrrrrRczt|||j|_t j |j |j|_tj|}d|_ d|_ d|_ t||j|_tj|}d|_ d|_ |j|_t||j|_|j$|_t j&|j(|j$|_|j-d|_yr)r/r0rXrQrrrrrrrrrrrrrr/rerWr7ryrrrs r:r0z MT5ForQuestionAnswering.__init__L s  ll6#4#4fnnE v.$)!#( ,1) < v.$(!,1)$*$=$=! <  ++))F$6$68I8IJ #r;c|jSrcr%rs r:rz,MT5ForQuestionAnswering.get_input_embeddingsg r&r;c~||_|jj||jj|yrcr(rs r:rz,MT5ForQuestionAnswering.set_input_embeddingsk r)r;c|jSrcr+rs r:r,z#MT5ForQuestionAnswering.get_encoderq r-r;c|jSrcr/rs r:r0z#MT5ForQuestionAnswering.get_decoderu r-r;r7rprrorqrr9rr:start_positions end_positionsrr;rrrrrgcf||n|jj}| | n|jj} | | d} | | | td|j |}| | n|jj} ||n|jj}|O|M|jj |jj k(r tjtt|}||j||| ||||}nI|rGt|ts7t|dt|dkDr|dndt|dkDr|dnd}|d}|j||| d||||| ||| }|d}|j!|}|j#dd \}}|j%d j'}|j%d j'}d}| | t| j)dkDr*| j%d j+|j,} t| j)dkDr*| j%d j+|j,} |j)d}| j/d|} | j/d|} t1| }||| }||| }||zdz }|s||f|ddz|z}||f|zS|St3||||j4|j6|j8|j:|j<|j6|j8 S)a start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence are not taken into account for computing the loss. Returns: NFrr=rr(r=r>) rprrrr r rrrrrrr>rr_) rb start_logits end_logitsrr@rArrBr rC)rRrrrrrrrr/_MT5ForQuestionAnswering__HEAD_MASK_WARNING_MSGrrrdrrr/ryrArrrr@rr r rrrHrrr)r6rprrorqrr9rr:rrrr;rrrrrHrErdr0rr total_loss ignored_indexrf start_lossend_lossrgs r:rJzMT5ForQuestionAnswering.forwardx sxB&1%)F  U !% 1 1) < !*!6IDKK{{%%)G)GG 5}E$-!  ""ll#-+#"3%9'+OO_!M-"1!"4474H14Loa0RV14_1E1I?1-tO (* ,,'1/ "/#1'!5/!5#'  *!,1#)<<r<#: j#++B/::< ''+668   &=+D?'')*Q."1"9"9""="@"@ATAT"U=%%'(1, - 5 5b 9 < C $D>C 'tn!C "d^#C $ uU&&')LL M%C nAC r;r)rr~rrrr}rlr)Zrjrrr:rtypingrrrrr2rtorch.nnrr r activationsr cache_utilsr rrr generationrmodeling_attn_mask_utilsrmodeling_outputsrrrrrrrmodeling_utilsr pytorch_utilsrrutilsrrrr r!r"r#r$r%utils.model_parallel_utilsr&r'configuration_mt5r)!torch.nn.attention.flex_attentionr*integrations.flex_attentionr+ get_loggerrKrrK_CHECKPOINT_FOR_DOCrrModuler-rQrjrrr|rrrr_rarlrMT5_START_DOCSTRINGrJr}__HEAD_MASK_WARNING_MSGr}r~rrrr__all__rr;r:rs // AA!PP)>.Q   L( !;J   H %! B*+299+4ryy.BII<&a299aJ!BII!J#RYY#NaryyaHgVBII&r!r!l}!}@*[z# Lc` !` ` FMObcp&"4op&dp&f mC(C CL } #5} } @ @ 2@ @ F w 0w w t r;