o Zh@sdZddlZddlZddlZddlZddlmZmZmZm Z ddl Z ddl m Z ddl m Z mZmZddlmZddlmZmZmZdd lmZdd lmZdd lmZmZmZmZmZm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&ddl'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/ddl0m1Z1m2Z2ddl3m4Z4e,rddl5m6Z6ddl7m8Z8e/9e:Z;dZZ?Gddde j>Z@Gddde j>ZAGddde j>ZBGddde j>ZCGd d!d!e j>ZDGd"d#d#e j>ZEGd$d%d%e j>ZFd&d'ZGGd(d)d)e j>ZHe+Gd*d+d+e#ZIGd,d-d-eIZJd.ZKe+Gd/d0d0eIZLe+d1d2Gd3d4d4eIeZMe+Gd5d6d6eIZNe+d7d2Gd8d9d9eIZOe+Gd:d;d;eIZPe+GdZRdS)?zPyTorch mT5 model.N)ListOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)Cache DynamicCacheEncoderDecoderCache)GenerationMixin)AttentionMaskConverter)BaseModelOutput)BaseModelOutputWithPastAndCrossAttentionsSeq2SeqLMOutputSeq2SeqModelOutput#Seq2SeqQuestionAnsweringModelOutputSeq2SeqSequenceClassifierOutputTokenClassifierOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) DUMMY_INPUTS DUMMY_MASKadd_start_docstringsauto_docstringis_torch_flex_attn_availableis_torch_fx_proxyis_torchdynamo_compilinglogging)assert_device_mapget_device_map) MT5Config) BlockMask)make_flex_block_causal_maska 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() ``` cs&eZdZdfdd ZddZZS) MT5LayerNormư>cs&ttt||_||_dS)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/var/www/auris/lib/python3.10/site-packages/transformers/models/mt5/modeling_mt5.pyr,|s  zMT5LayerNorm.__init__cCs\|tjdjddd}|t||j}|jjtj tj fvr)||jj}|j|S)NT)Zkeepdim) tor.float32powmeanZrsqrtr1r0dtypefloat16Zbfloat16)r2 hidden_statesZvariancer7r7r8forwards  zMT5LayerNorm.forward)r*)__name__ __module__ __qualname__r,rB __classcell__r7r7r5r8r){sr)c*eZdZdeffdd ZddZZS)MT5DenseActDenseconfigcsTttj|j|jdd|_tj|j|jdd|_t|j |_ t |j |_ dSNFbias)r+r,rLineard_modeld_ffwiwoDropout dropout_ratedropoutr dense_act_fnactr2rIr5r7r8r,s zMT5DenseActDense.__init__cCsl||}||}||}t|jjtjr/|j|jjjkr/|jjjtj kr/| |jjj}||}|SN) rPrVrT isinstancerQr0r.Tensorr?int8r;r2rAr7r7r8rBs    zMT5DenseActDense.forwardrCrDrEr&r,rBrFr7r7r5r8rHsrHcrG)MT5DenseGatedActDenserIcsjttj|j|jdd|_tj|j|jdd|_tj|j|jdd|_t |j |_ t |j |_dSrJ)r+r,rrMrNrOwi_0wi_1rQrRrSrTr rUrVrWr5r7r8r,s zMT5DenseGatedActDense.__init__cCsz|||}||}||}||}t|jjtjr6|j |jjj kr6|jjj tj kr6| |jjj }||}|SrX) rVr_r`rTrYrQr0r.rZr?r[r;)r2rAZ hidden_geluZ hidden_linearr7r7r8rBs   zMT5DenseGatedActDense.forwardr]r7r7r5r8r^sr^crG) MT5LayerFFrIcsJt|jrt||_nt||_t|j|jd|_ t |j |_ dS)Nr4)r+r,Z is_gated_actr^DenseReluDenserHr)rNlayer_norm_epsilon layer_normrrRrSrTrWr5r7r8r,s   zMT5LayerFF.__init__cCs&||}||}|||}|SrX)rercrT)r2rAZforwarded_statesr7r7r8rBs  zMT5LayerFF.forwardr]r7r7r5r8ras racsleZdZ  ddedeeffdd ZddZedd d Z dddZ         dddZ Z S) MT5AttentionFNrI layer_idxcst|j|_||_|j|_|j|_|j|_|j|_|j |_ |j |_ |j |j|_ ||_|dur@|jr@td|jjdtj|j|j dd|_tj|j|j dd|_tj|j|j dd|_tj|j |jdd|_|jrxt|j|j |_t|_d|_dS)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.FrK)r+r, is_decoderhas_relative_attention_biasrelative_attention_num_bucketsrelative_attention_max_distancerNd_kvkey_value_proj_dim num_headsn_headsrSrT inner_dimrglogger warning_oncer6rCrrMqkvo Embeddingrelative_attention_biasset pruned_headsgradient_checkpointingr2rIrirgr5r7r8r,s.  zMT5Attention.__init__cCst|dkrdSt||j|j|j\}}t|j||_t|j||_t|j||_t|j |dd|_ |jt||_|j|j|_ |j ||_dS)Nrr%dim) lenrrormrzrrsrtrurvrpunion)r2headsindexr7r7r8 prune_headss zMT5Attention.prune_headsT cCsd}|r|d}||dktj|7}t|}n t|t| }|d}||k}|t||t||||tj}t|t ||d}|t |||7}|S)a Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=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) rr9r%) r;r.longabsminZ zeros_likelogfloatmathZ full_likewhere)relative_position bidirectional num_buckets max_distanceZrelative_bucketsZ max_exactZis_smallZrelative_position_if_larger7r7r8_relative_position_buckets*  z&MT5Attention._relative_position_bucketc Cs|dur |jjj}|durtj|tj|ddddf}n |dddf|}tj|tj|ddddf}||}|j||j |j |j d}||} | gd d} | S)z%Compute binned relative position biasN)r?device)rrr)r9rr%r) rxr0rr.arangerr;rrhrjrkZpermuteZ unsqueeze) r2 query_length key_lengthrcache_positionZcontext_positionZmemory_positionrZrelative_position_bucketvaluesr7r7r8 compute_biasAs    zMT5Attention.compute_biasc Cs|jdd\} } |du} ||}|| d|j|jdd}|dur4|j|j}| r1|j }n|j }| r8|n|}| rO|durO|rO|j |j}|j |j}nE| |}||}|| d|j|jdd}|| d|j|jdd}|dur| s}| nd} ||||jd| i\}}| rd|j|j<t||dd}|dur|jd}|dur|n| dd}|jstjd|j| |f|j|jd }|jr|jrd|_n|j|||j| d }|dddd| dddf}|dur|ddddddd|jdf}||}|jr%t|jd}d |t|j<|dd|f}n|}||7}tjj |!dd "|}tjj#||j#|jd }|durL||}t||}|dd$}|| d|j%}|&|}|||f}| rt||f}|S)z Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). Nr9r:r%rTr rr?)rrrr})ptraining)'shapersviewrorm transpose is_updatedgetrgZcross_attention_cacheself_attention_cacheZ key_cacheZ value_cachertruupdater.matmulriZzerosrr?r{rZ requires_gradrrzr/listboolrZ functionalZsoftmaxrZtype_asrT contiguousrprv)r2rAmaskkey_value_states position_biaspast_key_valuelayer_head_maskr use_cacheoutput_attentionsr batch_size seq_lengthZis_cross_attentionZ query_statesrZcurr_past_key_valueZcurrent_statesZ key_statesZ value_statesZscoresrZreal_seq_length causal_maskZposition_bias_maskedZ attn_weightsZ attn_outputoutputsr7r7r8rBUsx           " &     zMT5Attention.forwardFN)Trr)NN) NNNNNNFFN) rCrDrEr&rintr,r staticmethodrrrBrFr7r7r5r8rfs,#  /rfcs@eZdZddeeffdd Z       d ddZZS) MT5LayerSelfAttentionFNrgcs>tt|||d|_t|j|jd|_t |j |_ dS)Nrirgrb) r+r,rf SelfAttentionr)rNrdrerrRrSrTr|r5r7r8r,s zMT5LayerSelfAttention.__init__c CsL||} |j| |||||||d} ||| d}|f| dd} | S)N)rrrrrrrrr%)rerrT) r2rAattention_maskrrrrrrnormed_hidden_statesattention_outputrr7r7r8rBs  zMT5LayerSelfAttention.forwardr)NNNNFFNrCrDrErrr,rBrFr7r7r5r8rs rcsBeZdZddeeffdd Z        d ddZZS) MT5LayerCrossAttentionNrgcs>tt|d|d|_t|j|jd|_t |j |_ dS)NFrrb) r+r,rfEncDecAttentionr)rNrdrerrRrSrT)r2rIrgr5r7r8r,s zMT5LayerCrossAttention.__init__Fc  CsP||} |j| |||||||| | d } ||| d} | f| dd}|S)N) rrrrrrrrrrr%)rerrT)r2rArrrrrrrrrrrZ layer_outputrr7r7r8rBs  zMT5LayerCrossAttention.forwardrX)NNNNFNFNrr7r7r5r8rs rcsJeZdZd deeffdd Z            d ddZZS) MT5BlockFNrgcs`t|j|_t|_|jt|||d|jr&|jt||d|jt |dS)Nr)rg) r+r,rhr ModuleListlayerappendrrrar|r5r7r8r,s   zMT5Block.__init__Tc Cs|jd||||| | | | d}|dd\}} |dd}|jtjkrDtt|t|jjdt|jj}tj || |d}|j oJ|du}|r|jd|||||| | dd| | d }|dd\}} |jtjkrtt|t|jjdt|jj}tj || |d}||dd}|jd|}|jtjkrtt|t|jjdt|jj}tj || |d}|f}| r|| f|}|S||}|S) Nr)rrrrrrrr9i)rmaxr%r:)rrrrrrrr) rr?r.r@risinfanyfinforclamprh)r2rArrencoder_hidden_statesencoder_attention_maskencoder_decoder_position_biasrcross_attn_layer_head_maskrrr return_dictrZself_attention_outputsZattention_outputsZ clamp_valueZdo_cross_attentionZcross_attention_outputsrr7r7r8rBsn           zMT5Block.forwardr) NNNNNNNNFFTNrr7r7r5r8rsrc Cs@zddl}ddl}ddl}Wn tytdwtj|}t d||j |}g}i} |D]\} } t d| d| |j || } | | | | | <q6|D]} | d} tdd | Dr{t d d| | | dqXd | d vrt d d| | | dqX|}| | } | D]}|d |r|d|}n|g}|ddvrt|d}n|ddkrt|d}|d}n|ddkrt|d}|d}n|ddkrt|d}|d}n|ddkrt|drt|d}nt|drt|d}n|ddkrt|d}n||ddks#|ddkr)t|d}nh|ddkr6t|d}n[|dd krE| dd!krEq|dd!krRt|d"}n?|dd#krrt|dkrr|drrt|d$|d}qz t||d}Wntyt d d| Yqwt|dkrt|d}||}q|ddvrt|d}|dd%krt d&| jd'| || } z|j| jksJd(|jd)| jd*Wnty}z |j|j| jf7_d}~wwt d+| t| |j |_!| | dqXt d,d-| "d.|S)/z'Load tf checkpoints in a pytorch model.rNzLoading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.z&Converting TensorFlow checkpoint from zLoading TF weight z with shape /css|]}|dvVqdS))Zadam_vZadam_mZAdamWeightDecayOptimizerZAdamWeightDecayOptimizer_1Z global_stepNr7).0nr7r7r8 s  z)load_tf_weights_in_mt5..z Skipping Z_slot_r:z [A-Za-z]+_\d+z_(\d+))Zkernelscale embeddingr0Zself_attentionrZenc_dec_attentionr%Zdense_relu_denser9Zrms_normrefinal_layer_normrZ output_biasbetarLZsquad classifierdecoderlogitslm_headrPZwi_rz"Transposing numpy weight of shape z for zPointer shape z and array shape mismatchedzInitialize PyTorch weight z%Weights not copied to PyTorch model: z, .)#renumpyZ tensorflow ImportErrorrqerrorospathabspathinfotrainZlist_variablesZ load_variablersplitrjoinpop fullmatchgetattrhasattrrisdigitAttributeErrorrrrAssertionErrorargsr.Z from_numpyZastyper<datakeys)modelrIZtf_checkpoint_pathrnptfZtf_pathZ init_varsnamesZ tf_weightsnamerarrayZtxt_nameZpointerZm_nameZ scope_namesnumer7r7r8load_tf_weights_in_mt5rs                                 *   rcs<eZdZdZdeffdd ZdejdejfddZZ S) MT5ClassificationHeadz-Head for sentence-level classification tasks.rIcsBtt|j|j|_tj|jd|_t|j|j |_ dS)N)r) r+r,rrMrNdenserRclassifier_dropoutrT num_labelsout_projrWr5r7r8r,s zMT5ClassificationHead.__init__rAreturncCs6||}||}t|}||}||}|SrX)rTrr.tanhrr\r7r7r8rBs     zMT5ClassificationHead.forward) rCrDrE__doc__r&r,r.rZrBrFr7r7r5r8rsrc@sTeZdZeZeZdZdZdZ dZ dZ dZ dgZ dgZeddZdd Zd d Zd S) MT5PreTrainedModel transformerTFrrQcCs$tt}tt}|||d}|S)N)decoder_input_ids input_idsdecoder_attention_mask)r.Ztensorrr)r2rZ input_mask dummy_inputsr7r7r8rs  zMT5PreTrainedModel.dummy_inputscCs |jj}t|tr|jj|dd St|ttt t fr^|j jjj d|ddt |dr>|jjs>|jjjj d|ddt |dr\|jjjj d||jjdd|jjjd Sd St|trt |dr}|jjjj d|dd|jjjd Sd St|tr|jjjj d||jjddt |jdr|jjd ur|jjj|jjjj d||jjddt |jdr|jjd ur|jjjd Sd Sd St|tr$|jjjj d||jjddt |jdr|jjd ur|jjj|jjjj d||jjddt |jdr |jjd ur"|jjjd Sd Sd St|tr|jjjj d||jjddt |jdrO|jjd urO|jjj|jjjj d||jjddt |jdrt|jjd urt|jjj|jjjj d||jjddt |jdr|jjd ur|jjjd Sd Sd St|t r|jj}|jj!}|jj"}|j#jjj d|||dd|j$jjj d||dd|j%jjj d||dd|j&jjj d|||dd|j'r|j(jjj d||ddd Sd Sd S) zInitialize the weights?)r>Zstdr qa_outputsrrLN))rIZinitializer_factorrYr)r0rZfill_MT5ModelMT5ForConditionalGenerationMT5EncoderModelMT5ForQuestionAnsweringsharedZnormal_rtie_word_embeddingsrr rNrLZzero_MT5ForTokenClassificationrrrrrHrPrQrOr^r_r`rfrlrnrsrtrurvrirx)r2modulefactorrNrmror7r7r8 _init_weightsst                     z MT5PreTrainedModel._init_weightscCs|jj}|jj}|durtdt|r1t|jddd|}tj||dddfgdd}n| |j}|dddf |dddf<||d<|durStd| |d k||S) Nzself.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id. See MT5 docs for more information.r:)r%.r}r%).rz1self.model.config.pad_token_id has to be defined.) rIdecoder_start_token_id pad_token_id ValueErrorr r.fullrcatZ new_zeroscloneZ masked_fill_)r2rrrZshifted_input_idsr7r7r8 _shift_rightEs    zMT5PreTrainedModel._shift_rightN)rCrDrEr& config_classrZload_tf_weightsZbase_model_prefixZis_parallelizableZsupports_gradient_checkpointingZ_supports_quantized_cacheZ_supports_static_cacheZ_supports_cache_classZ_no_split_modulesZ_keep_in_fp32_modulespropertyrrrr7r7r7r8rs  =rc seZdZdfdd ZeedddZeeddZdd Z d d Z             dd d Z dde e jdfde jde jdedef ddZede jdedede jde jdef ddZZS) MT5StackNcsxt||_j|_tfddtjD|_t j j d|_ t j|_|d|_d|_d|_dS)Ncs"g|] }tt|dk|dqS)rr)rr)rirIr7r8 js"z%MT5Stack.__init__..rbF)r+r, embed_tokensrhrrrange num_layersblockr)rNrdrrRrSrT post_initmodel_parallel device_mapr{)r2rIr#r5r!r8r,cs  zMT5Stack.__init__cCstdt|durtt|jttj n||_ t |j t|jd|_ d|j vr/dn dtt|j |_dtt|j |_|j D]\}}|D]}dt|}|j|||j|<qRqL|j|j|_|j|j|_dS)Na=`MT5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0, 'block.1': 1, ...}Tcpucuda:)warningswarn FutureWarningr$rr&r$r.cuda device_countr)r#r(rstrr first_devicer last_deviceitemsr;r#r)r2r)rtrurZ cuda_devicer7r7r8 parallelizevs"$ * zMT5Stack.parallelizecCsxtdtd|_d|_d|_d|_tt|j D] }|j | d|j |<q|j d|_ |j d|_ t jdS)N\Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.Fr*)r,r-r.r(r)r2r3r$rr&r;r#rr.r/ empty_cache)r2r r7r7r8 deparallelizeszMT5Stack.deparallelizecC|jSrXr#r2r7r7r8get_input_embeddingszMT5Stack.get_input_embeddingscC ||_dSrXr:r2Znew_embeddingsr7r7r8set_input_embeddings zMT5Stack.set_input_embeddingsc+Cs0|jrtj|j|j|j|_| dur| n|jj} | dur"| n|jj } | dur,| n|jj } | dur6| n|jj } |durT|durT|j rGdnd}t d|d|d|dure|}|d|d}n|durr|dd}n|j rwdnd}t d|d|d |jr|jr| rtd d } |dur|jdurt d ||}|\}}| d ur|j st d|dd }d }|j r| s|durt|trt|tsd }t|t}n#t|tsd }tdt|}n|durttt}n|j sd}|dur|nd}| durtj||||jd} |dur,ts,||}tj|||jd}|jj rC|||| |dur>|jnd| }n&|durg|ddddddf}|j|j d}d|t!|j j"}nd}|j r|dur|\}}}||f}|durtj||jtj#d}|$|}nd}|%||jj&}|%||jj&}| rdnd}| rdnd}| r|j rdnd}d}d} |'|}!t(|j)D] \}"}#||"}$||"}%|jr0tj|!j|dur||!j}|dur||!j}|dur||!j}|dur||!j}| dur| |!j} |$dur%|$|!j}$|%dur0|%|!j}%| r8||!f}|jrT|jrT|*|#j+|!||||| |$|%d| | | | }&n|#|!||||| |$|%|| | | | d }&| d urx|&ddd|&dd}&|&dd\}!}'|&d}|j r|dur|&| rdnd} | r||&df}|j r||&df}|jr|j,-D]!\}(})|"|)dkrdt.|(|j/kr|!dt.|(d}!qq|0|!}!|'|!}!| r||!f}| r|'nd}*|r|j}*|r|1}*| st2dd |!|*|||fDSt3|!|*|||d!S)"NZdecoder_zYou cannot specify both zinput_ids and zinputs_embeds at the same timer:zYou have to specify either z input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fz.)last_hidden_statepast_key_valuesrA attentionscross_attentions)4r(r.r/ set_devicer2r#r;rIrroutput_hidden_statesuse_return_dictrhrsizerr{rrqrrrYr rr Zfrom_legacy_cacheget_seq_lengthrrr!r/_update_causal_maskrr?rrrZinvert_attention_maskZ get_head_maskr%rT enumerater&Z_gradient_checkpointing_funcrBr)r4r1r3rZto_legacy_cachetupler)+r2rrrrrC head_maskcross_attn_head_maskrHrrrLrrZerr_msg_prefixZ input_shaperrZreturn_legacy_cacheZreturn_self_attention_cachepast_key_values_lengthZmask_seq_lengthrZencoder_batch_sizeZencoder_sequence_length_Zencoder_hidden_shapeZencoder_extended_attention_maskZall_hidden_statesZall_attentionsZall_cross_attentionsrrrAr Z layer_modulerrZ layer_outputsZnext_decoder_cachertruZ next_cacher7r7r8rBsl                             "    zMT5Stack.forwardFrr' input_tensorrrHrc Cs:|jjdkr|dur|dkr|SdS|jjdkr&t|tjr$t|}|S|dur.|nd}|dur7|jnd}|jjdkrO|sO|sOt j ||||j drOdS|j }|j d} |r^|} nt|tjri|j d n|| d} |j|| | |||j dd } |jjdkr|dur|jjd vr|st|j} t | | } | S) NZflash_attention_2rZflex_attentionrFZsdpa)rCrUZ is_trainingr%r:)sequence_length target_lengthr?rr)r/ZxpuZnpu)rIZ_attn_implementationrrYr.rZr(rOZis_compileablerZ_ignore_causal_mask_sdparr?rZget_max_cache_shape5_prepare_4d_causal_attention_mask_with_cache_positionrtyperrZ_unmask_unattended) r2rrWrrHrZpast_seen_tokensZusing_compilable_cacher?rXrYr min_dtyper7r7r8rPsT           zMT5Stack._update_causal_maskrXrYr?rc KsD|dur|dkr|}|St|j}tj||f|||jd}|dkr+tj|dd}|tj||jd|ddk9}|ddddddf |ddd}|dur| }|j d} |ddddddd| f|ddddddf |j} | dk} |ddddddd| f | ||ddddddd| f<|S) aM 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. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. NrE)Z fill_valuer?rr%)ZdiagonalrDr:r)r~r.rrrrZtriurZreshapeexpandrrr;Z masked_fill) rrXrYr?rrkwargsrr\Z mask_lengthZ padding_maskr7r7r8rZs,  $ 6  z>MT5Stack._prepare_4d_causal_attention_mask_with_cache_positionrX) NNNNNNNNNNNNN)F)rCrDrEr,rPARALLELIZE_DOCSTRINGr5DEPARALLELIZE_DOCSTRINGr8r<r@rBrr.rZr rrPrrr?rZrFr7r7r5r8rbsb     Dra_ 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)`. c&speZdZdZdZeZdgZddgZdeffdd Z e e d+d d Z e e d d ZddZddZddZddZddZe d,deejdeejdeejdeejdeejdeejdeejdeeeejd eeeejd!eejd"eejd#eed$eed%eed&eed'eejd(eeejeff"d)d*ZZ S)-r aw Examples: ```python >>> from transformers import MT5Model, AutoTokenizer >>> model = MT5Model.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, return_tensors="pt") >>> labels = tokenizer(text_target=summary, return_tensors="pt") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```mt5Fdecoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weightencoder.embed_tokens.weightdecoder.embed_tokens.weightrIcst|t|j|j|_t|}d|_ d|_ d|_ t ||j|_ t|}d|_ d|_ |j|_t ||j|_|d|_d|_dSNFT)r+r,rrw vocab_sizerNrcopydeepcopyrhris_encoder_decoderrencodernum_decoder_layersr%rr'r(r)r2rIencoder_configZdecoder_configr5r7r8r,?s    zMT5Model.__init__NcCsptdt|durtt|jjttj n||_ t |j t|jj|j |j |j |j d|_dS)NaL`T5Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0': 0, 'encoder.block.1': 1, ...}T)r,r-r.r$rrjr&r$r.r/r0r)r#r5rr(r2r)r7r7r8r5Vs  zMT5Model.parallelizecCsVtdt|j|j|jd|_|jd|_d|_d|_t j dSNr6r*F) r,r-r.rjr8rr;r(r)r.r/r7r;r7r7r8r8js  zMT5Model.deparallelizecCr9rXrr;r7r7r8r<zr=zMT5Model.get_input_embeddingscC"||_|j||j|dSrXrrjr@rr?r7r7r8r@~ zMT5Model.set_input_embeddingscCr9rXrjr;r7r7r8 get_encoderr=zMT5Model.get_encodercCr9rXrr;r7r7r8 get_decoderr=zMT5Model.get_decodercCs*|D]\}}|jj|j|qdS) Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)r4rjrZ attentionrr2Zheads_to_prunerrr7r7r8 _prune_headsszMT5Model._prune_headsrrrrrSdecoder_head_maskrTencoder_outputsrHrCdecoder_inputs_embedsrrrLrrrcCs| dur| n|jj} |dur|n|jj}|dur,|dur,|jj|jjkr,ttt|}|dur=|j ||| || ||d}n$|rat |t sat |dt |dkrR|dndt |dkr]|dndd}|d}|j rtj|jj||jj}|dur||jj}|dur||jj}|dur||jj}|j||| | ||||| | |||d }|s||St|j|j|j|j|j|j|j|jdS) a 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). 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. 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**. 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 ```NrrrCrSrrLrrr%r9rGrArI rrrCrHrrrSrTrrrLrr)rGrHdecoder_hidden_statesdecoder_attentionsrJencoder_last_hidden_staterencoder_attentions)rIrrMr%rkr,r-Z _MT5Model__HEAD_MASK_WARNING_MSGr.rjrYrrr(r.r/rKrr2r;rrGrHrArIrJ)r2rrrrrSr{rTr|rHrCr}rrrLrrrAdecoder_outputsr7r7r8rBsvR  zMT5Model.forwardrXNNNNNNNNNNNNNNNN)!rCrDrEr model_typer&r"_keys_to_ignore_on_load_unexpected_tied_weights_keysr,rr_r5r`r8r<r@rurwrzrrr. LongTensor FloatTensor BoolTensorrZrrrrrBrFr7r7r5r8r &s       r z; MT5 Model with a `language modeling` head on top. )Z custom_introc(seZdZdZdZeZdgZgdZdeffdd Z e e d1d d Z e e d d Zd dZddZddZddZddZddZe                 d2deejdeejdeejdeejdeejdeejdeejd eeeejd!eeeejd"eejd#eejd$eejd%eed&eed'eed(eed)eejd*eeejeff$d+d,Z d$ejfd-d.Z!d/d0Z"Z#S)3r a Examples: ```python >>> from transformers import MT5ForConditionalGeneration, AutoTokenizer >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt") >>> outputs = model(**inputs) >>> loss = outputs.loss ```rarb)rcrdzlm_head.weightrIcst||j|_t|j|j|_t |}d|_ d|_ d|_ t ||j|_t |}d|_ d|_ |j|_t ||j|_tj|j|jdd|_|d|_d|_dS)NFTrK)r+r,rN model_dimrrwrfrrgrhrhrrirrjrkr%rrMrr'r(r)rlr5r7r8r,Js"    z$MT5ForConditionalGeneration.__init__NcCstdt|durtt|jjttj n||_ t |j t|jj|j |j |j |j |j|jj|_d|_dS)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)r,r-r.r$rrjr&r$r.r/r0r)r#r5rrr;r2r(rnr7r7r8r5es  z'MT5ForConditionalGeneration.parallelizecCsdtdt|j|j|jd|_|jd|_|jd|_d|_d|_ t j dSro) r,r-r.rjr8rr;rr(r)r.r/r7r;r7r7r8r8zs  z)MT5ForConditionalGeneration.deparallelizecCr9rXrpr;r7r7r8r<r=z0MT5ForConditionalGeneration.get_input_embeddingscCrqrXrrr?r7r7r8r@rsz0MT5ForConditionalGeneration.set_input_embeddingscCr>rXrr?r7r7r8set_output_embeddingsrAz1MT5ForConditionalGeneration.set_output_embeddingscCr9rXrr;r7r7r8get_output_embeddingsr=z1MT5ForConditionalGeneration.get_output_embeddingscCr9rXrtr;r7r7r8rur=z'MT5ForConditionalGeneration.get_encodercCr9rXrvr;r7r7r8rwr=z'MT5ForConditionalGeneration.get_decoderrrrrrSr{rTr|rHrCr}labelsrrrLrrrcCs| dur| n|jj} |dur|n|jj}|dur,|dur,|jj|jjkr,ttt|}|dur=|j ||| ||||d}n$|rat |t sat |dt |dkrR|dndt |dkr]|dndd}|d}|j rptj|jj| dur|dur| dur|| }|j rtj|jj||jj}|dur||jj}|dur||jj}|dur||jj}|j||| | ||||| ||||d }|d}|j rtj|j j|j|j j|_||jjj}|jjr||jd}||}d}| durtd d }| |j} ||d |d | d }|s1|f|dd|}|dur/|f|S|St|||j|j|j |j!|j"|j|j d S) a 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). 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. 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**. 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]` 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%r9rrr rZ ignore_indexr: lossrrHrrrJrrr)#rIrrMr%rkr,r-Z3_MT5ForConditionalGeneration__HEAD_MASK_WARNING_MSGr.rjrYrrr(r.r/rKrr2rr;rr0rrrrrrNrrHrArIrJrG)r2rrrrrSr{rTr|rHrCr}rrrrLrrrArsequence_outputZ lm_logitsrloss_fctoutputr7r7r8rBsY       z#MT5ForConditionalGeneration.forwardcCs ||SrX)r)r2rr7r7r8%prepare_decoder_input_ids_from_labelsfrAzAMT5ForConditionalGeneration.prepare_decoder_input_ids_from_labelsc Cs|dur td|Sd}|D]M}d}|D]}||d||jf}q|dj|djkr@td|djd|djdt|t|krWtdt|dt|d||f}q|S) NzHYou might want to consider setting `use_cache=True` to speed up decodingr7rz%reordered_layer_past_states[0] shape z and layer_past_states[0] shape rz&length of reordered_layer_past_states z! and length of layer_past_states )rqwarningZ index_selectr;rrrr)r2rHZbeam_idxZreordered_decoder_pastZlayer_past_statesZreordered_layer_past_statesZlayer_past_stater7r7r8_reorder_cachejs(  z*MT5ForConditionalGeneration._reorder_cacherX)NNNNNNNNNNNNNNNNN)$rCrDrErrr&rrrr,rr_r5r`r8r<r@rrrurwrrr.rrrrZrrrrrBrrrFr7r7r5r8r .s        Ar cseZdZdZdZeZdgZdeffdd Ze e ddd Z e e d d Z d d ZddZddZddZe       ddeejdeejdeejdeejdeedeedeedeeejeffddZZS) r a 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 ```rarcrIcsZt|t|j|j|_t|}d|_ d|_ t ||j|_ | d|_d|_dSNF)r+r,rrwrfrNrrgrhrrirrjr'r(r))r2rIrmr5r7r8r,s   zMT5EncoderModel.__init__NcCsbtdt|durtt|jjttj n||_ t |j t|jj|j |j d|_dS)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)r,r-r.r$rrjr&r$r.r/r0r)r#r5r(rnr7r7r8r5s  zMT5EncoderModel.parallelizecCs>tdt|j|jd|_d|_d|_tj dSro) r,r-r.rjr8r;r(r)r.r/r7r;r7r7r8r8s zMT5EncoderModel.deparallelizecCr9rXrpr;r7r7r8r<r=z$MT5EncoderModel.get_input_embeddingscCs||_|j|dSrX)rrjr@r?r7r7r8r@sz$MT5EncoderModel.set_input_embeddingscCr9rXrtr;r7r7r8rur=zMT5EncoderModel.get_encodercCs0|D]\}}|jj|jdj|qdS)rxrN)r4rjr&rrrryr7r7r8rzszMT5EncoderModel._prune_headsrrrSrCrrLrrc Cs0|dur|n|jj}|j|||||||d}|S)aJ 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). 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 ```Nr~)rIrMrj) r2rrrSrCrrLrr|r7r7r8rBs# zMT5EncoderModel.forwardrX)NNNNNNN)rCrDrErrr&rrr,rr_r5r`r8r<r@rurzrrr.rrrrrrrBrFr7r7r5r8r sN    r z MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c$seZdZdgZddgZdeffdd Ze               ddee j d ee j d ee j d ee j d ee j d ee j dee j dee e j dee j dee j dee j deedeedeedeedeeeff ddZZS)MT5ForSequenceClassificationrbrcrdrIcs2t|t||_t||_|d|_dSr)r+r,r rrclassification_headr'r(rWr5r7r8r,!s    z%MT5ForSequenceClassification.__init__NrrrrrSr{rTr|rCr}rrrrLrrcCsh|dur|n|jj}| durd} |dur!| dur!td|jj|dur6| dur6|dur1td||}|j||||||||| | | | ||d}|d}||jj  |j }t t |ddkrhtd|j\}}}||ddf|d |ddd ddf}||}d}| dur| |j } |jjdur|jjdkrd |j_n|jjdkr| jt jks| jt jkrd |j_nd |j_|jjd krt}|jjdkr||| }n-||| }n'|jjd krt}||d |jj| d }n|jjd krt}||| }|s |f|dd}|dur|f|S|St|||j|j|j|j |j!|j"|j#d S)am 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). 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. 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**. 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). 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`.) rrrrSr{rTr|rCr}rrrLrrr%z7All examples must have the same number of tokens.r:Z regressionZsingle_label_classificationZmulti_label_classificationr)$rIrMNotImplementedErrorr6rCrrreqZ eos_token_idr;rrr.Zunique_consecutivesumrrrZ problem_typerr?rrr squeezerrrrHrrrJrrr)r2rrrrrSr{rTr|rCr}rrrrLrrrZeos_maskrrVr3Zsentence_representationrrrrr7r7r8rB+s?   ,      $      z$MT5ForSequenceClassification.forward)NNNNNNNNNNNNNNN)rCrDrErrr&r,rrr.rrZrrrrrrrBrFr7r7r5r8rsj       rcseZdZdgZdeffdd Ze        ddeej deej deej d eej d eej d ee d ee d ee de e ej e ffddZZS)rz'transformer.encoder.embed_tokens.weightrIcsJt||j|_t||_t|j|_t |j |j|_ | dSrX) r+r,rr rrrRrrTrMr3rr'rWr5r7r8r,s   z"MT5ForTokenClassification.__init__NrrrSrCrrrLrrc  Cs|dur|n|jj}|j|||||||d} | d} || } || } d} |dur:t} | | d|j|d} |sO| | ddf}| durM| f|S|St| | | j | j dS)a> input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./t5#training). labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. N)rrSrCrrLrrr:r9)rrrArI) rIrMrrTrrrrrrArI)r2rrrSrCrrrLrrrArrrrr7r7r8rBs4  z!MT5ForTokenClassification.forward)NNNNNNNN)rCrDrErr&r,rrr.rZrrrrrBrFr7r7r5r8rs>   rc&s2eZdZdgZddgZdeffdd ZddZd d Zd d Z d dZ e                d#de e jde e jde e jde e jde e jde e jde e jde eee jde e jde e jde e jde e jde ede ede ede ed eee jeff"d!d"ZZS)$rrbrcrdrIcst||j|_t|j|j|_t |}d|_ d|_ d|_ t ||j|_t |}d|_ d|_ |j|_t ||j|_|j|_t|j|j|_|d|_dSre)r+r,rNrrrwrfrrgrhrhrrirrjrkr%rrrMr3r r'r(rlr5r7r8r, s"    z MT5ForQuestionAnswering.__init__cCr9rXrpr;r7r7r8r<- r=z,MT5ForQuestionAnswering.get_input_embeddingscCrqrXrrr?r7r7r8r@1 rsz,MT5ForQuestionAnswering.set_input_embeddingscCr9rXrtr;r7r7r8ru7 r=z#MT5ForQuestionAnswering.get_encodercCr9rXrvr;r7r7r8rw; r=z#MT5ForQuestionAnswering.get_decoderNrrrrrSr{rTr|start_positions end_positionsrCr}rrrLrrcCs|dur|n|jj}| dur| n|jj} | dur| durd} |dur3| dur3|dur.td||}| dur9| n|jj} |durC|n|jj}|dur_|dur_|jj|jjkr_tt t |}|durp|j ||| ||||d}n$|rt |t st |dt|dkr|dndt|dkr|dndd}|d}|j||| d||||| |||d }|d}||}|jdd d \}}|d }|d }d}| dur| durt| dkr| d |j} t| dkr| d |j} |d}| d|} | d|} t|d }||| }||| }||d}|s8||f|dd|}|dur6|f|S|St||||j|j|j|j|j|j|jd S)a= input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. T5 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 [T5 Training](./t5#training). 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) T5 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 [T5 Training](./t5#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. 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**. NFrr~rr%r9r) rrrCrHrrrSrTrrrLrr:r}r) r start_logits end_logitsrHrrrJrrr)rIrMrrrr%rkr,r-Z/_MT5ForQuestionAnswering__HEAD_MASK_WARNING_MSGr.rjrYrrrr rrrrNr;rrrrrHrArIrJrG)r2rrrrrSr{rTr|rrrCr}rrrLrrArrrrrZ total_lossZ ignored_indexrZ start_lossZend_lossrr7r7r8rB> s=           zMT5ForQuestionAnswering.forwardr)rCrDrErrr&r,r<r@rurwrrr.rrrrZrrrrrBrFr7r7r5r8r sx     r)r r rrrr r)Srrgrrr,typingrrrrr.rZtorch.nnrrr Z activationsr Z cache_utilsr r rZ generationrZmodeling_attn_mask_utilsrZmodeling_outputsrrrrrrrZmodeling_utilsrZ pytorch_utilsrrutilsrrrrrr r!r"Zutils.model_parallel_utilsr#r$Zconfiguration_mt5r&Z!torch.nn.attention.flex_attentionr'Zintegrations.flex_attentionr(Z get_loggerrCrqr_r`Moduler)rHr^rarfrrrrrrrZ__HEAD_MASK_WARNING_MSGr r r rrr__all__r7r7r7r8s    $ (    !f%'dkq? X %K W