The kogito from epfl-nlp

DistilBERTRelationMatcher - TypeError: new() missing 1 required positional argument: 'task'

When attempting to use the DistilBERTRelationMatcher class from the kogito.core.processors.relation module, I encountered the following error:

from kogito.core.processors.relation import DistilBERTRelationMatcher

DistilBERTRelationMatcher(name='distilbert_matcher')

Error: TypeError: new() missing 1 required positional argument: 'task'

Could you please provide advice on how to solve this issue? It seems that the DistilBERTRelationMatcher class does not support the task argument.

I encountered the same problem when attempting to use the BERTRelationMatcher and SWEMRelationMatcher classes as well.

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Hi, thanks for the fantastic work. I have a problem here. I want to train COMET on my own dataset, and I follow the user guide from here docs. My codes are as follows:

import os

from kogito.core.knowledge import KnowledgeGraph
from kogito.models.bart.comet import COMETBART, COMETBARTConfig, AutoTokenizer

path = 'data/csv'

config = COMETBARTConfig(
    output_dir="bart",
    num_workers=16,
    learning_rate=1e-5,
    gpus=1,
    sortish_sampler=True,
    atomic=True,
    max_epochs=56,
    pretrained_model="facebook/bart-large",
)
model = COMETBART(config)
model.tokenizer = AutoTokenizer.from_pretrained("mismayil/comet-bart-ai2")

train_graph = KnowledgeGraph.from_csv(os.path.join(path, 'train.csv'))
val_graph = KnowledgeGraph.from_csv(os.path.join(path, 'val.csv'))
test_graph = KnowledgeGraph.from_csv(os.path.join(path, 'test.csv'))

model.train(train_graph=train_graph, val_graph=val_graph, test_graph=test_graph)

# Save as a pretrained model
model.save_pretrained("comet-bart/v1")

However, i got following errors when i run the code:

  File "/home/wzh/work/comet_ft/cometbart_train.py", line 25, in <module>
    model.train(train_graph=train_graph, val_graph=val_graph, test_graph=test_graph)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/kogito/models/bart/comet.py", line 109, in train
    trainer: pl.Trainer = generic_train(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/kogito/models/bart/utils.py", line 262, in generic_train
    trainer.fit(model)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
    self._call_and_handle_interrupt(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
    self._dispatch()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
    self.training_type_plugin.start_training(self)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
    self._results = trainer.run_stage()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
    return self._run_train()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1319, in _run_train
    self.fit_loop.run()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
    self.epoch_loop.run(data_fetcher)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 193, in advance
    batch_output = self.batch_loop.run(batch, batch_idx)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/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/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 215, in advance
    result = self._run_optimization(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 266, in _run_optimization
    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step
    lightning_module.optimizer_step(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1652, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 164, in step
    trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 339, in optimizer_step
    self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 163, in optimizer_step
    optimizer.step(closure=closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 68, in wrapper
    return wrapped(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/optim/optimizer.py", line 140, in wrapper
    out = func(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/transformers/optimization.py", line 457, in step
    loss = closure()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 148, in _wrap_closure
    closure_result = closure()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 160, in __call__
    self._result = self.closure(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 155, in closure
    self._backward_fn(step_output.closure_loss)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 327, in backward_fn
    self.trainer.accelerator.backward(loss, optimizer, opt_idx)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 314, in backward
    self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 91, in backward
    model.backward(closure_loss, optimizer, *args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1434, in backward
    loss.backward(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/_tensor.py", line 488, in backward
    torch.autograd.backward(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/autograd/__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Here is my environment：

Nvidia RTX A6000
Cuda 11.6
Use conda to create python environment

I do not kown why, any reply will be appreciated.
Thanks.

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DistilBERTRelationMatcher - TypeError: new() missing 1 required positional argument: 'task'

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

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