Entity Linking gives error while training
Hi,

I followed this tutorial completely:
https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/nlp/Entity_Linking_Medical.ipynb#scrollTo=gO3t67PnmtL7

Now, I'm following these steps to train model on entire UMLs data:
!conda install python=3.7.13 --y
!pip install Cython
!python -m pip install  'git+https://github.com/NVIDIA/NeMo.git@r1.10.0#egg=nemo_toolkit[all]'
!pip install -q faiss-gpu
!pip install -q folium==0.2.1

!pip install -q imgaug==0.2.5
!git clone https://github.com/NVIDIA/apex
!cd apex
!pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex
!python "/home/ml-002/Fatima/NeMo/examples/nlp/entity_linking/data/umls_dataset_processing.py" --project_dir "./NeMo/examples/nlp/entity_linking"
!python self_alignment_pretraining.py  project_dir=.

However, as soon as epoch 0 reaches 1% I get this error:
Epoch 0:   1%| | 16870/2147821 [5:23:29<681:01:39,  1.15s/it, loss=0.093, v_num=
                                                                                Epoch 0, global step 7000: 'val_loss' reached 0.31622 (best 0.31622), saving model to '/home/ml-002/Fatima/NeMo/examples/nlp/entity_linking/medical_entity_linking_experiments/sap_bert_umls/2022-07-07_20-20-04/checkpoints/sap_bert_umls--val_loss=0.3162-epoch=0.ckpt' as top 3
Epoch 0:   1%| | 17283/2147821 [5:25:50<669:28:28,  1.13s/it, loss=0.0938, v_num[NeMo I 2022-07-08 01:46:18 multi_similarity_loss:91] Encountered zero loss in multisimloss, loss = 0.0. No hard examples found in the batch
Error executing job with overrides: ['project_dir=.']
Traceback (most recent call last):
  File "self_alignment_pretraining.py", line 38, in main
    trainer.fit(model)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
    self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 721, in _call_and_handle_interrupt
    return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
    return function(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run
    results = self._run_stage()
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
    return self._run_train()
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1353, in _run_train
    self.fit_loop.run()
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
    self._outputs = self.epoch_loop.run(self._data_fetcher)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance
    batch_output = self.batch_loop.run(batch, batch_idx)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
    outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 207, in advance
    self.optimizer_idx,
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization
    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step
    using_lbfgs=is_lbfgs,
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 1595, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/core/lightning.py", line 1646, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/core/optimizer.py", line 168, in step
    step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/strategies/ddp.py", line 286, in optimizer_step
    optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step
    return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/plugins/precision/native_amp.py", line 85, in optimizer_step
    closure_result = closure()
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in __call__
    self._result = self.closure(*args, **kwargs)
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 134, in closure
    step_output = self._step_fn()
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 437, in _training_step
    training_step_output, self.trainer.accumulate_grad_batches
  File "/home/ml-002/anaconda3/envs/py39_ft/lib/python3.7/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 75, in from_training_step_output
    "In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present"
pytorch_lightning.utilities.exceptions.MisconfigurationException: In automatic_optimization, when `training_step` returns a dict, the 'loss' key needs to be present

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Epoch 0:   1%| | 17283/2147821 [5:25:53<669:33:18,  1.13s/it, loss=0.0938, v_num

I checked file and training_step does have loss key. What is the reason behind this error? How do I catch this exception and continue with execution? Please help!! I have been stuck on this for 2 weeks. 

Im using Ubuntu