:W&BKtM 2mch0162; 9 /0.29.0+7872fd9d9f86ca5c50617e19cb168122b7f959480.70.0 Wg" olxinve7uoxh 2mch0162 2mch0162juju11-uc-san-diegoSPAtop" _wandb{}  batch_size1024  dataset_limit0.2  dropout0.2864403919216048 epochs10  hidden_dim256 ( learning_rate0.00029210866040970394  num_attention_heads4  num_branch_encoder_layers7  num_embedding_layers10  num_encoder_layers6Bjjtttt_vanilla_2mch0162b88oquqqwj)jj-spatop-sweepagent-tttt-vanilla-1-k2mn8 3 (Hhp"3.12.10*0.29.0j linux-x86_64?qO7 2mch0162&  _runtime0  _wandb.runtime0pji -Linux-6.8.0-136-generic-x86_64-with-glibc2.36CPython 3.12.10 i*-of*=/data/spatop/options_files/sweep_tttt_jj/config_2mch0162.json*-l* /scratch/logs*-n*jjtttt_vanilla_2mch01622-m spanet.trainRjacksonjulian2014@gmail.comZ /scratch/logsb)jj-spatop-sweepagent-tttt-vanilla-1-k2mn8r/opt/conda/bin/python nnop6xezbzfca37zdpjitrwsd6n4o4ggXNVIDIA A100-SXM4-80GB /.ӽS NVIDIA A100-SXM4-80GB6"Ampere*(GPU-78f24734-e3a1-59a7-ce1b-bb4283f77c9713.2 nnop6xezbzfca37zdpjitrwsd6n4o4gg2  requirements.txt/W 2mch0162ZF (H (H (hp"3.12.10*0.29.0j linux-x86_644tY 2mch0162ZH (H (H (8hp"3.12.10*0.29.0j linux-x86_64u9j* 2mch0162b trainer/global_step2*\ 2mch0162ZK (H (H(8hp"3.12.10*0.29.0j linux-x86_64/ 2mch0162b*"trainer/global_step2kK 2mch0162j: *LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] \6i 2mch0162j    ┏━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓ ┃ ┃ Name ┃ Type ┃ Params ┃ Mode ┃ FLOPs ┃ ┡━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩ │ 0 │ embedding │ MultiInputVectorEmbed… │ 3.4 M │ train │ 0 │ │ 1 │ encoder │ JetEncoder │ 7.9 M │ train │ 0 │ │ 2 │ branch_decoders │ ModuleList │ 214 M │ train │ 0 │ │ 3 │ regression_decoder │ RegressionDecoder │ 0 │ train │ 0 │ │ 4 │ classification_decoder │ ClassificationDecoder │ 0 │ train │ 0 │ │ │ other params │ n/a │ 48 │ n/a │ n/a │ └───┴────────────────────────┴────────────────────────┴────────┴───────┴───────┘ `-1[ 2mch0162j  Trainable params: 226 M Non-trainable params: 86 Total params: 226 M Total estimated model params size (MB): 904.172 Modules in train mode: 5901 Modules in eval mode: 0 Total FLOPs: 0 Np 2mch0162j_  Pwandb: WARNING Config item 'hidden_dim' was locked by 'sweep' (ignored update). z 2mch0162ji  Zwandb: WARNING Config item 'num_embedding_layers' was locked by 'sweep' (ignored update). N< Jx 2mch0162jg  Xwandb: WARNING Config item 'num_encoder_layers' was locked by 'sweep' (ignored update). U 2mch0162jn  _wandb: WARNING Config item 'num_branch_encoder_layers' was locked by 'sweep' (ignored update). "W8y 2mch0162jh  Ywandb: WARNING Config item 'num_attention_heads' was locked by 'sweep' (ignored update). ue_s 2mch0162jb  Swandb: WARNING Config item 'dataset_limit' was locked by 'sweep' (ignored update). 8Np 2mch0162j_ Pwandb: WARNING Config item 'batch_size' was locked by 'sweep' (ignored update). 4s 2mch0162jb Swandb: WARNING Config item 'learning_rate' was locked by 'sweep' (ignored update). m 2mch0162j\ Mwandb: WARNING Config item 'dropout' was locked by 'sweep' (ignored update). 9l 2mch0162j[ Lwandb: WARNING Config item 'epochs' was locked by 'sweep' (ignored update). Fb 2mch0162*  transformer_dim256  transformer_dim_scale2.0  initial_embedding_dim256  position_embedding_dim256 ! num_branch_embedding_layers3  num_jet_embedding_layers0  num_jet_encoder_layers2  num_detector_layers2  num_regression_layers3  num_classification_layers3  split_symmetric_attention1 ! transformer_activation"gelu"  skip_connections1 ( "initial_embedding_skip_connections1  linear_block_type"GRU"  transformer_type"Gated"  linear_activation"gelu"  normalization "LayerNorm"  masking "Filling" linear_prelu_activationtrue  use_pairwise_interactions0  num_pairwise_features4  pairwise_embedding_dim8  pairwise_input_source""  use_moefalse  num_experts8  num_experts_per_tok2  moe_loss_scale0.01 F event_info_file2"/data/spatop/event_files/tttt/tttt_hadronic.yaml" = training_file+"/data/spatop/tttt_15M/h5/tttt_training.h5"  validation_file""  testing_file""  normalize_features1  limit_to_num_jets0  balance_particles0  balance_jets0 ! balance_classificationsfalse  partial_events1  dataset_randomization0  train_validation_split0.95  limit_val_batches1.0  num_dataloader_workers0  mask_sequence_vectors1  combine_pair_loss"min"  optimizer"AdamW"  focal_gamma0.0  combinatorial_scale0.0 # learning_rate_warmup_epochs1.0  learning_rate_cycles1  assignment_loss_scale1.0  detection_loss_scale1.0  kl_loss_scale0.0  regression_loss_scale0.0 ! classification_loss_scale0.0 ! use_mass_classificationfalse  mass_classes[]  balance_lossestrue  l2_penalty0.0002  gradient_clip10.0 num_gpu1  verbose_outputfalse  usable_gpus""  trial_time"" $ trial_output_dir"./test_output" 2mch0162j ؑ/opt/conda/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead. M 2mch0162j Ё/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:434: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.  2mch0162j ܛ:/opt/conda/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead. MkO 2mch0162j ë:/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:434: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance. Et 2mch0162jc ^REpoch 0/9 0/302 0:00:01 • -:--:-- 0.00it/s v_num: 0162դ8# 2mch0162j ^ M 2mch0162j  ȭk9C! 2mch0162j2 ̬k#Traceback (most recent call last): xq" 2mch0162j  kAagZ# 2mch0162jI k: File "", line 198, in _run_module_as_main H$ 2mch0162j  k4OI%O% 2mch0162j> k/ File "", line 88, in _run_code C & 2mch0162j  k-8x' 2mch0162jg kX File "/opt/conda/lib/python3.12/site-packages/spanet/train.py", line 277, in n[( 2mch0162j  ؔkK$) 2mch0162j k Q$wD* 2mch0162j3 k$main(**parser.parse_args().__dict__)V= !+ 2mch0162j l , 2mch0162j  ГlVLAt- 2mch0162jc lT File "/opt/conda/lib/python3.12/site-packages/spanet/train.py", line 207, in main ^ S. 2mch0162j  l $/ 2mch0162j l H0 2mch0162j7 l(trainer.fit(model, ckpt_path=checkpoint)+k!1 2mch0162j l ں2 2mch0162j  ؝lȈ3 2mch0162jw lh File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 584, in fit БE4 2mch0162j  m&ZR#$5 2mch0162j ؄m @e@6 2mch0162j/ m call._call_and_handle_interrupt(d~!7 2mch0162j m P 8 2mch0162j  ȥm2~&9 2mch0162j ém{ File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 49, in _call_and_handle_interrupt ˌyQ: 2mch0162j  my$; 2mch0162j m ;B< 2mch0162j1 m"return trainer_fn(*args, **kwargs)f9bw!= 2mch0162j m KǴ!> 2mch0162j  m#$_!? 2mch0162j m ٜ!@ 2mch0162j m z!A 2mch0162j m  !B 2mch0162j m S^!C 2mch0162j n q7!D 2mch0162j n /^!E 2mch0162j ؍n X\ !F 2mch0162j n A!G 2mch0162j n y1!H 2mch0162j n kn0!I 2mch0162j ˦n S/!J 2mch0162j n^!K 2mch0162j Іn^2D!L 2mch0162j ͵n^!M 2mch0162j n^d6!N 2mch0162j ȩn^!O 2mch0162j n^/~4!P 2mch0162j n^s!Q 2mch0162j n^S !R 2mch0162j n^*!S 2mch0162j n^'!T 2mch0162j n^u-!U 2mch0162j n^+!V 2mch0162j n^؋!W 2mch0162j n^"S!X 2mch0162j n^/Ax!Y 2mch0162j o^ߏG!Z 2mch0162j o^C2![ 2mch0162j ؍o^S8!\ 2mch0162j o^I L!] 2mch0162j o^^!^ 2mch0162j o^!_ 2mch0162j رo^zi!` 2mch0162j o^!a 2mch0162j ͺo^!b 2mch0162j o^l!!c 2mch0162j ȗo^ME!d 2mch0162j Ьo^/!e 2mch0162j o PM8f 2mch0162j  o jg 2mch0162j} on File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 630, in _fit_impl G;h 2mch0162j  o%X<$i 2mch0162j o L`j 2mch0162jO o@self._run(model, ckpt_path=ckpt_path, weights_only=weights_only).!k 2mch0162j o *Vl 2mch0162j  p~m 2mch0162jy ňpj File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 1079, in _run {6n 2mch0162j  ݢp?s$o 2mch0162j p 0;p 2mch0162j* ͯpresults = self._run_stage()uP@}!q 2mch0162j p Mc%r 2mch0162j  pJ!s 2mch0162j p !t 2mch0162j p 12!u 2mch0162j p T!v 2mch0162j p Tcl!w 2mch0162j p -(+!x 2mch0162j p !y 2mch0162j p xv!z 2mch0162j ཅq M$!{ 2mch0162j ̍q (!| 2mch0162j ʓq F!} 2mch0162j q yG!~ 2mch0162j ʠq ~g! 2mch0162j q 62" 2mch0162j Ѐq ZE " 2mch0162j q^RqA" 2mch0162j ֱq^" 2mch0162j q^n E" 2mch0162j q^" 2mch0162j ӿq^3" 2mch0162j q^9J" 2mch0162j q^mc" 2mch0162j ػq^"4" 2mch0162j q^" 2mch0162j q^AN>" 2mch0162j q^3Z" 2mch0162j ؋q^Xy" 2mch0162j q^>4" 2mch0162j q^_" 2mch0162j q^F" 2mch0162j r^" 2mch0162j r^}#6" 2mch0162j r  2mch0162j  בrH) 2mch0162j ٘rp File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 1123, in _run_stage  2mch0162j  rm$% 2mch0162j r 14 2mch0162j" rself.fit_loop.run()" 2mch0162j r {  2mch0162j  r 2mch0162jv ؚrg File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 217, in run  2mch0162j  r% 2mch0162j r ݡ/ 2mch0162j rself.advance()" 2mch0162j r yI\ 2mch0162j  s[" 2mch0162jz •sk File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 469, in advance  2mch0162j  su#% 2mch0162j s oH 2mch0162j6 s'self.epoch_loop.run(self._data_fetcher)PY" 2mch0162j t 7q( 2mch0162j  t>ŵ 2mch0162j ڜtr File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 153, in run ǔM 2mch0162j  t5% 2mch0162j t F-; 2mch0162j) tself.advance(data_fetcher) " 2mch0162j t ͻ 2mch0162j  tp 2mch0162j ؂tv File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 352, in advance EQj 2mch0162j  tIe% 2mch0162j t k?y 2mch0162jg tXbatch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)'#c" 2mch0162j u ] 2mch0162j  uEZc" 2mch0162j ُu {S" 2mch0162j u 1L" 2mch0162j u ܊|" 2mch0162j u WJ" 2mch0162j u Ci" 2mch0162j ȯu /4" 2mch0162j u 6" 2mch0162j u T(R" 2mch0162j еu ;K" 2mch0162j u " 2mch0162j u =$" 2mch0162j زu s윩" 2mch0162j u " 2mch0162j u ^" 2mch0162j u ݱ" 2mch0162j u dj_" 2mch0162j u ^" 2mch0162j u ta" 2mch0162j u /" 2mch0162j u^Z" 2mch0162j u^" 2mch0162j u^M1" 2mch0162j ˁv^aPWf" 2mch0162j ȉv^p" 2mch0162j ˎv^" 2mch0162j v^3" 2mch0162j ؒv^ m" 2mch0162j v^dO" 2mch0162j Ѯv^ 5" 2mch0162j Ȉv^" 2mch0162j Зv^_<" 2mch0162j ^W" 2mch0162j ^z9" 2mch0162j ^ÈY" 2mch0162j ^ # 2mch0162j ^jٶ# 2mch0162j І^X# 2mch0162j ^5v# 2mch0162j ǎ^r# 2mch0162j 䔀^a# 2mch0162j ^Ep6# 2mch0162j ^z~# 2mch0162j ß^#[# 2mch0162j ^# 2mch0162j ^A # 2mch0162j ^uz0# 2mch0162j ^Kq# 2mch0162j ଱^|J(# 2mch0162j ^'# 2mch0162j Ā^K<Q# 2mch0162j Ȁ^$# 2mch0162j Њ̀^ 4# 2mch0162j Ѐ^f*# 2mch0162j Ӏ^@x# 2mch0162j ׀^&# 2mch0162j ހ^?ml# 2mch0162j ^{# 2mch0162j ^v# 2mch0162j ^S# 2mch0162j Ќ^@%# 2mch0162j ^-# 2mch0162j  N  2mch0162j ȸH 2mch0162j w File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 239, in optimizer_step  2mch0162j ȣ& 2mch0162j  sXc 2mch0162jn ⤁^return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)9a`# 2mch0162j  \  2mch0162j ڻP# 2mch0162j  og# 2mch0162j ̻ # 2mch0162j Х # 2mch0162j Á ,# 2mch0162j Ɂ ں # 2mch0162j ́ f# 2mch0162j с ,v\# 2mch0162j ց %fw0# 2mch0162j ہ bS# 2mch0162j  O# 2mch0162j О !/# 2mch0162j ^H# 2mch0162j ^# 2mch0162j ^W# 2mch0162j ^"# 2mch0162j ^c# 2mch0162j ^ uP# 2mch0162j ӄ^ey# 2mch0162j ^k;C# 2mch0162j ^Pi# 2mch0162j ^H# 2mch0162j ^EG# 2mch0162j ^q# 2mch0162j Ț^%r# 2mch0162j 㞂^ˀ# 2mch0162j ^_N&# 2mch0162j ^x+# 2mch0162j ࠯^y # 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2mch0162j ȮР +Ud# 2mch0162j ՠ $# 2mch0162j ڠ Hwi# 2mch0162j ߠ 4{# 2mch0162j  J# 2mch0162j  ; # 2mch0162j  O\# 2mch0162j  >?# 2mch0162j  =#i# 2mch0162j  s# 2mch0162j ^"fl# 2mch0162j ^Z“R# 2mch0162j 蟉^5d!# 2mch0162j ؿ^@n# 2mch0162j Ò^ayo# 2mch0162j ^.k# 2mch0162j Ⱦ^l# 2mch0162j ^#rv# 2mch0162j ^s# 2mch0162j ڬ^# 2mch0162j ı^$Lv# 2mch0162j ܶ^03b# 2mch0162j 艺^ # 2mch0162j ɡ^fqWK# 2mch0162j Ρ^#h# 2mch0162j ӡ^ b# 2mch0162j ȍء^# 2mch0162j ݡ^M)B# 2mch0162j ^pN# 2mch0162j 諂 Q,  2mch0162j  2mch0162j v File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 391, in training_step k$  2mch0162j ٧d& 2mch0162j  i] 2mch0162jK ;return self.lightning_module.training_step(*args, **kwargs)W/# 2mch0162j  MS  2mch0162j Ţv# 2mch0162j Ȣ q# 2mch0162j ̢ 3# 2mch0162j Ѣ x # 2mch0162j Ыآ u# 2mch0162j ߢ Q # 2mch0162j  F-# 2mch0162j  -# 2mch0162j ԰ E# 2mch0162j Ј N# 2mch0162j  %# 2mch0162j ؑ w# 2mch0162j ȱƣ^8# 2mch0162j ͣ^=# 2mch0162j ң^_g# 2mch0162j أ^p# 2mch0162j ݣ^Պ# 2mch0162j ^i9# 2mch0162j ^# 2mch0162j Ȫ^M/# 2mch0162j ^Ƌ'# 2mch0162j ^# 2mch0162j В^Š# 2mch0162j ^`*# 2mch0162j ԓ^o# 2mch0162j ▤^/.# 2mch0162j ^Jc# 2mch0162j ^ ,# 2mch0162j ^]G# 2mch0162j ^49# 2mch0162j ֯^cW# 2mch0162j 첤^ȼ# 2mch0162j ^י# 2mch0162j ^$ح# 2mch0162j Ⱦ^wǡ# 2mch0162j ȴ¤^/"# 2mch0162j Ǥ^zi|# 2mch0162j ͤ^u4# 2mch0162j Ѥ^)# 2mch0162j Ԥ^O@m# 2mch0162j פ^5# 2mch0162j ۤ^J?ə# 2mch0162j ^sf# 2mch0162j ^Xr# 2mch0162j ^a# 2mch0162j 𱄥^!# 2mch0162j Ѓ^-# 2mch0162j ^U6`# 2mch0162j ^O# 2mch0162j ৗ^# 2mch0162j ^\# 2mch0162j ^5# 2mch0162j ^OX# 2mch0162j ^†# 2mch0162j ׮^+R# 2mch0162j ^U)# 2mch0162j ^# 2mch0162j ^)W")# 2mch0162j ĥ^M\# 2mch0162j ʥ^f v3# 2mch0162j ЮΥ^6# 2mch0162j ӥ^i# 2mch0162j ܥ^# 2mch0162j ^@# 2mch0162j  lk  2mch0162j ] 2mch0162j  File "/opt/conda/lib/python3.12/site-packages/spanet/network/jet_reconstruction/jet_reconstruction_training.py", line 214, in training_step ׻I  2mch0162j ԅa& 2mch0162j 𐌦 G 2mch0162j5 ␦%outputs = self.forward(batch.sources)h/# 2mch0162j ؐߧ   2mch0162j $# 2mch0162j  K癌# 2mch0162j  P<# 2mch0162j  uu# 2mch0162j  Aa# 2mch0162j  ؏# 2mch0162j  dj# 2mch0162j Ȭ # 2mch0162j  4,# 2mch0162j  t6# 2mch0162j  ~# 2mch0162j  EA# 2mch0162j  oQX# 2mch0162j  [:G# 2mch0162j  CBS# 2mch0162j 賅 е=# 2mch0162j  Q d3X 2mch0162j  :# 2mch0162j  . # 2mch0162j ^g # 2mch0162j ɝ^_# 2mch0162j ^B# 2mch0162j ĩ^$" -# 2mch0162j ꬮ^78# 2mch0162j ְ^o`# 2mch0162j ^-# 2mch0162j ^GK# 2mch0162j ^ b2# 2mch0162j ^# 2mch0162j î^G# 2mch0162j Ȯ^ք# 2mch0162j ͮ^oƠu# 2mch0162j Ү^T# 2mch0162j ׮^Le# 2mch0162j ڮ^ڍ# 2mch0162j ݮ^n/r# 2mch0162j ^Ŧ# 2mch0162j ^ep*# 2mch0162j ^Ӭ=# 2mch0162j ^~G# 2mch0162j ^ $O# 2mch0162j ^C# 2mch0162j ^&͵# 2mch0162j ^;r# 2mch0162j ʅ^r# 2mch0162j ʊ^{# 2mch0162j 药^# 2mch0162j 䑯^K9# 2mch0162j ^M# 2mch0162j ^>&# 2mch0162j 䞯^ܠ޵# 2mch0162j  J  2mch0162j 䫯Ya 2mch0162jv f File "/opt/conda/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl 7t  2mch0162j ϯX& 2mch0162j կ $F 2mch0162j4 ۯ$return forward_call(*args, **kwargs)1T# 2mch0162j  ]!  2mch0162j é7"# 2mch0162j  5fv# 2mch0162j  KQ:# 2mch0162j  La# 2mch0162j  K # 2mch0162j 衅 jS# 2mch0162j  ܵ# 2mch0162j  7# 2mch0162j  /# 2mch0162j Ƙ # 2mch0162j ܛ # 2mch0162j  S# 2mch0162j ^*,c# 2mch0162j 奰^d# 2mch0162j ^BF# 2mch0162j ^IH# 2mch0162j ̰^B# 2mch0162j ^Ԅ5# 2mch0162j ^Hs# 2mch0162j ^a~# 2mch0162j İ^Q1# 2mch0162j ʰ^1j1# 2mch0162j ΰ^e5# 2mch0162j Ӱ^# 2mch0162j Фڰ^^# 2mch0162j ݰ^ł# 2mch0162j ^<`C# 2mch0162j ^~# 2mch0162j ^% # 2mch0162j ^nF# 2mch0162j ^ڞ&# 2mch0162j Ƞ^-ġ# 2mch0162j ^o# 2mch0162j ^k-# 2mch0162j З^.X# 2mch0162j د^yɘ# 2mch0162j ꑱ^# 2mch0162j ▱^@#L# 2mch0162j ؈^.K# 2mch0162j ^## 2mch0162j ֢^d7Z# 2mch0162j 㨱 Mk  2mch0162j qqy 2mch0162j 鳱o File "/opt/conda/lib/python3.12/site-packages/spanet/network/layers/branch_decoder.py", line 128, in forward o  2mch0162j Ʊ{LC& 2mch0162j ̱  2mch0162jn ұ^encoded_vectors, particle_vector, _ = self.encoder(event_vectors, padding_mask, sequence_mask)# 2mch0162j ױ C`2  2mch0162j !y# 2mch0162j  # 2mch0162j  # 2mch0162j ؀ ?!# 2mch0162j  ۦ# 2mch0162j  # 2mch0162j  W'$# 2mch0162j 膈 i4# 2mch0162j ؠ O G# 2mch0162j  ̐# 2mch0162j 嚲 # 2mch0162j  GQ# 2mch0162j 裨 M # 2mch0162j ۭ r%9# 2mch0162j  $Y# 2mch0162j 𝷲 '# 2mch0162j ໲ f# 2mch0162j  # 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2mch0162j Ü^# 2mch0162j ^c׷# 2mch0162j ^sW# 2mch0162j ת^# 2mch0162j Ͱ^p?# 2mch0162j ؊^>+# 2mch0162j ȭ^yS# 2mch0162j ȼ %  2mch0162j Ƕ# 2mch0162j~ ̶n File "/opt/conda/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl MC  2mch0162j 0y3y& 2mch0162j  PQ|I 2mch0162j7 'return self._call_impl(*args, **kwargs)hc# 2mch0162j  5  2mch0162j I# 2mch0162j  u# 2mch0162j  dDQ# 2mch0162j  Rz# 2mch0162j  .9# 2mch0162j ؠ G # 2mch0162j  0# 2mch0162j  :V# 2mch0162j  ǚN# 2mch0162j  ߨ&# 2mch0162j з # 2mch0162j 䘸 ^)# 2mch0162j ^# 2mch0162j ^w# 2mch0162j ^j # 2mch0162j ٳ^۱# 2mch0162j ^h# 2mch0162j ý^͂T*# 2mch0162j Ÿ^M7# 2mch0162j Ѹ^SkO# 2mch0162j ո^dyf# 2mch0162j ٸ^?;# 2mch0162j ߸^+# 2mch0162j л^fY# 2mch0162j ^# 2mch0162j ^ə4# 2mch0162j ^.i# 2mch0162j ف^ # 2mch0162j Æ^h# 2mch0162j ^Z# 2mch0162j ^~# 2mch0162j ^ # 2mch0162j ^0ۣ# 2mch0162j ĝ^0O# 2mch0162j ^O# 2mch0162j ^X# 2mch0162j ^]# 2mch0162j п^zߧ# 2mch0162j ב^t9t# 2mch0162j ^Am# 2mch0162j ^v# 2mch0162j ^x# 2mch0162j 㧺^֨ƃ# 2mch0162j 䬺^ # 2mch0162j  q,  2mch0162j ޻t̗ 2mch0162jv f File "/opt/conda/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl ^  2mch0162j ߺ@x& 2mch0162j  h8F 2mch0162j4 $return forward_call(*args, **kwargs)lc?# 2mch0162j ؠ u  2mch0162j 1# 2mch0162j  =# 2mch0162j ك W# 2mch0162j  # 2mch0162j Ȋ # 2mch0162j  "# 2mch0162j  8@# 2mch0162j  e# 2mch0162j  l# 2mch0162j  # 2mch0162j  mg# 2mch0162j ͮ _35# 2mch0162j ^1# 2mch0162j Ч^xV# 2mch0162j 鼻^ C# 2mch0162j ^(aS# 2mch0162j ƻ^`XT# 2mch0162j ̻^Z# 2mch0162j л^_K<# 2mch0162j ջ^Co# 2mch0162j ڻ^FS# 2mch0162j ^# 2mch0162j Њª^ƭ# 2mch0162j ʪ^| # 2mch0162j Ъ^Oe# 2mch0162j Ȥ֪^# 2mch0162j ܪ^g# 2mch0162j ^eO# 2mch0162j ^[# 2mch0162j ^?_d# 2mch0162j ^o# 2mch0162j ؉^,O# 2mch0162j ^yt[# 2mch0162j ^Dg# 2mch0162j ȓ^@N# 2mch0162j И^"-}# 2mch0162j ^̈́+B# 2mch0162j 颫^?'o# 2mch0162j ܩ^XC`Q# 2mch0162j 쮫^T-X# 2mch0162j ^⽺# 2mch0162j Ⱥ V  2mch0162j   2mch0162j ƫo File "/opt/conda/lib/python3.12/site-packages/spanet/network/layers/stacked_encoder.py", line 81, in forward kQ^  2mch0162j Te& 2mch0162j  WZ• 2mch0162j 蹀rcombined_vectors, gate_logits_list = self.encoder(combined_vectors, combined_padding_mask, combined_sequence_mask)LS# 2mch0162j Ԅ > m  2mch0162j q# 2mch0162j  3M# 2mch0162j ʒ ص# 2mch0162j  ;V:# 2mch0162j  ## 2mch0162j  2R# 2mch0162j  r# 2mch0162j ի \M# 2mch0162j  # 2mch0162j  k# 2mch0162j ، |# 2mch0162j  \h# 2mch0162j  A# 2mch0162j  B# 2mch0162j 赨 @~# 2mch0162j  # 2mch0162j  ]# 2mch0162j û c# 2mch0162j  Dl# 2mch0162j ŭ !P# 2mch0162j ʭ |# 2mch0162j ϭ ar# 2mch0162j ٭ ^s# 2mch0162j  ?p# 2mch0162j  H# 2mch0162j  g# 2mch0162j  # 2mch0162j  T];# 2mch0162j  T# 2mch0162j  Z[# 2mch0162j ׅ <## 2mch0162j 𪌮 +# 2mch0162j ^ݙj# 2mch0162j ^s# 2mch0162j ܠ^b}X# 2mch0162j ^DSE# 2mch0162j ^3:# 2mch0162j ɮ^# 2mch0162j ִ^޽# 2mch0162j ^`# 2mch0162j ؾ^4## 2mch0162j Ů^%\# 2mch0162j ʮ^)# 2mch0162j Ϯ^_`F# 2mch0162j ծ^{# 2mch0162j ڮ^Ii# 2mch0162j ^*# 2mch0162j ؉^Om# 2mch0162j ^CYo# 2mch0162j ^$n# 2mch0162j ^g# 2mch0162j ^ݪ# 2mch0162j ^qS# 2mch0162j 앯^Z# 2mch0162j ^-edz# 2mch0162j à^E b# 2mch0162j ٣^{Z# 2mch0162j ^qd# 2mch0162j ^=3# 2mch0162j ^ˈ# 2mch0162j ͼ^%֢# 2mch0162j į^̵# 2mch0162j ʯ^Ԇ# 2mch0162j ϯ^uE# 2mch0162j ׯ^?X# 2mch0162j ݯ^:8 . ) _wandbruntime12  _runtime121S (