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Setting `use_cache=False`...Fc>^UUU4SjnU$)Nc>T"/UQTPTP76$rr)inputsmodulerrs rEcustom_forwardKSbertEncoder.forward..create_custom_forward..custom_forwards&% 9v 9~ 9&7 99rGr)rrrrs` rEcreate_custom_forward3SbertEncoder.forward..create_custom_forwards9*)rGrr rrec30# UH nUcMUv M g7frr).0vs rE 'SbertEncoder.forward..-s" %q  %s )last_hidden_statepast_key_valuesr attentionscross_attentions) rCr enumeraterrtrainingloggerwarningr9utils checkpointtupler )rBrrrrrr use_cacheroutput_hidden_states return_dictall_hidden_statesall_self_attentionsall_cross_attentionsnext_decoder_cachei layer_modulelayer_head_maskr layer_outputsrs ` @rErSSbertEncoder.forwards#7BD$5b4 4;;#B#B HL $-R$(4OA|#$58I$I!.7.CilO%1-7; **t}}NNt!&I*!& 6 6 A A),7!"#)* ! !-!"#)*"%! *!,M"}R'8&;;"  &9!!$=('(#;;222+?%a(C,,,(e 5j 14E E  "!#$ %   ,+.+*1   rG)rCrr) NNNNNNFFTrr\s@rErrs1,"#"Y Y rGrc.^\rSrSrU4SjrSrSrU=r$) SbertPooleri=c>[TU]5 [R"URUR5Ul[R "5Ulgr)r&r'r(rkr+rTanh activationrAs rEr'SbertPooler.__init__?s9 YYv1163E3EF '')rGc\USS2S4nURU5nURU5nU$)Nr)rr!)rBrfirst_token_tensor pooled_outputs rErSSbertPooler.forwardDs6+1a40 #56  6 rG)r!rrr\s@rErr=s$ rGrcd^\rSrSrSr\rSrSrS/r U4Sjr Sr S Sjr \ U4S j5rS rU=r$) SbertPreTrainedModeliMzz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. bertTrc b>[TU]"UR40UD6 [[U]U5 gr)r&r' name_or_pathr )rBrCkwargsrDs rEr'SbertPreTrainedModel.__init__Xs* ,,77 eT#F+rGc[U[R5(akURRR SUR RS9 URb%URRR5 gg[U[R5(axURRR SUR RS9 URb2URRURR5 gg[U[R5(aJURRR5 URRRS5 gg)zInitialize the weightsg)meanstdNg?)rr(rkweightdatanormal_rCinitializer_rangebiaszero_r)rr2fill_)rBrs rE _init_weights"SbertPreTrainedModel._init_weights\s fbii ( ( MM   & &dkk;; ' ={{&   &&('  - - MM   & &dkk;; ' =!!- ""6#5#56<<>.  - - KK   " " $ MM   $ $S ).rGc<[U[5(aX!lggr)rrr)rBrrns rE_set_gradient_checkpointing0SbertPreTrainedModel._set_gradient_checkpointingns fl + +,1 ) ,rGc >URSS5nURSS5n[X#40UD6nUc[S0UD6nU"U5nU$[[U]"SSU0UD6nU$)a$Instantiate the model. Args: kwargs: Input args. model_dir: The model dir used to load the checkpoint and the label information. num_labels: An optional arg to tell the model how many classes to initialize. Method will call utils.parse_label_mapping if num_labels is not input. label2id: An optional label2id mapping, which will cover the label2id in configuration (if exists). Returns: The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained model_dirNcfgpretrained_model_name_or_pathr)poprrr&r from_pretrained)clsr,r>r? model_argsrCmodelrDs rE _instantiate!SbertPreTrainedModel._instantiatersJJ{D1 jj%*9DVD   .:.FKE %5G.7G;EGE rGr)F)rUrVrWrXrYr config_classbase_model_prefixsupports_gradient_checkpointing_keys_to_ignore_on_load_missingr'r8r; classmethodrFrZr[r\s@rEr(r(MsG L&*#'6&7#,*$2rGr(cx\rSrSr%Sr\R \S'Sr\ \ \ \R 4\S'Sr \ \S'Srg))AttentionBackboneModelOutputWithEmbeddingiNembedding_outputlogitsr,r)rUrVrWrXrOr9 FloatTensor__annotations__rPrrrr,dictrZrrGrErNrNs<*.e''.8`__ subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config (:class:`~modelscope.models.nlp.structbert.SbertConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder` argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an input to the forward pass. rCc >[TU]U5 Xl[U5Ul[ U5UlU(a [U5OSUlUR5 gr) r&r'rCrrRrencoderrpooler init_weights)rBrCadd_pooling_layerr,rDs rEr'SbertModel.__init__sI   )&1#F+ ->k&)D  rGc.URR$rrRr-)rBs rEget_input_embeddingsSbertModel.get_input_embeddingss...rGc$XRlgrr^)rBrns rEset_input_embeddingsSbertModel.set_input_embeddingss */'rGcUR5H7up#URRURR U5 M9 g)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)itemsrXrrr)rBheads_to_prunerrs rE _prune_headsSbertModel._prune_headss< +002LE LL  u % / / ; ;E B3rGc U bU OURRn U bU OURRn U bU OURRn URR(aU bU OURR n OSn UbUb [ S5eUbUR5nO"UbUR5SSnO [ S5eUunnUb URO URnU bU SSRSOSnUc[R"UUU-4US9nUcs[URS 5(a4URRSS2SU24nURUU5nUnO$[R "U[R"US 9nUR%X/U5nURR(aEUbBUR5unnnUU4nUc[R"UUS9nUR'U5nOSnUR)UURR*5nURUUUUUS S 9unnUR-UUUUUU U U U U S 9 nUSnUR.bUR/U5OSn U (dUU 4USS-U4-$[1UU UR2UR4UR6UR8US9$)u Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~modelscope.models.nlp.structbert.SbertTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(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**. token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, 1]``: - 0 corresponds to a `sentence A` token, - 1 corresponds to a `sentence B` token. position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1]``. head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`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 (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.ModelOutput` instead of a plain tuple. encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). Returns: Returns `modelscope.outputs.AttentionBackboneModelOutputWithEmbedding` Examples: >>> from modelscope.models import Model >>> from modelscope.preprocessors import Preprocessor >>> model = Model.from_pretrained('damo/nlp_structbert_backbone_base_std', task='backbone') >>> preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_backbone_base_std') >>> print(model(**preprocessor('这是个测试'))) NFzDYou cannot specify both input_ids and inputs_embeds at the same timer z5You have to specify either input_ids or inputs_embedsrre)r$r!r"T)rJrr!rKrLrM) rrrrrrrrrr)r pooler_outputrrrr rO)rCrruse_return_dictrqrrgr?r$shaper9onesrIrRr!r;r>r@get_extended_attention_maskinvert_attention_mask get_head_maskrrXrYrNrrrr )!rBrJrr!rrrKrrrrrrrr,rN batch_sizerOr$rLrPrQextended_attention_maskencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapeencoder_extended_attention_maskrOoriginal_embedsencoder_outputssequence_outputr%s! rErSSbertModel.forwardsw|2C1N-TXT_T_TqTq$8$D KK , , &1%V  "#..*K  &',,.s3KGI I"- J%.%:!! @T@T"-"1!3A!6! :  7$6$;$= %-).(*9&.2.H.H&/( +/3 +&&y'+{{'D'DF -1OO%)'#9!% -<- )/,, 2"7#B+/!5#'  *!, $ 7 =A #!#%4QR%89rsP"Y !" +7&/,;+.<>>' T-biiT-nPPf bii  1RYY1h   "))  ZZzb 299b J "))  >:>B 0L  F4E4EFA.%A.GA.rG