Datasets:

seyonec

/

goodscents_leffingwell

like 0

Hi! Could you provide your code snippet?

Hi! Could you provide your code snippet?

Thanks for getting back to me! This error occurs when I just try to run ‘load_dataset(“seyonec/goodscents_leffingwell”)’

I can't reproduce your bug with datasets 2.5.2, could you try upgrading your version of datasets?

I can't reproduce your bug with datasets 2.5.2, could you try upgrading your version of datasets?

Will try a previous version, thanks!

this now works, thank you so much for the help! :)

No problem :)

No problem :)

I'm now running into a weird issue when calling trainer.train() with the labels (a list of 0s, 1s, or nulls for 152 tasks) - some kind of type mismatch? I made sure to cast any labels that are not null to int type so I am a little bit confused. Thanks again for all your help!

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[10], line 1
----> 1 train_results = trainer.train()

File /anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1534     self.model_wrapped = self.model
   1536 inner_training_loop = find_executable_batch_size(
   1537     self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1538 )
-> 1539 return inner_training_loop(
   1540     args=args,
   1541     resume_from_checkpoint=resume_from_checkpoint,
   1542     trial=trial,
   1543     ignore_keys_for_eval=ignore_keys_for_eval,
   1544 )

File /anaconda/envs/dgllife/lib/python3.8/site-packages/accelerate/utils/memory.py:136, in find_executable_batch_size..decorator(*args, **kwargs)
    134     raise RuntimeError("No executable batch size found, reached zero.")
    135 try:
--> 136     return function(batch_size, *args, **kwargs)
    137 except Exception as e:
    138     if should_reduce_batch_size(e):

File /anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/trainer.py:1787, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
...
    return self.collate_fn(data)
  File "/anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/models/graphormer/collating_graphormer.py", line 132, in __call__
    batch["labels"] = torch.from_numpy(np.stack([i["labels"] for i in features], axis=0))
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.

Which version of transformers do you use?

Which version of transformers do you use?

4.31.0!

Would you recommend using an older version? It seems to complain that I am trying to convert a numpy array of objects to a tensor but I'm not sure where they are getting casted as objects

Hi @seyonec , no, it should be good, there was a bug on Graphormer I fixed prior to this one.
I suspect the problem happens with the null values you get that cannot be converted to int and are considered as numpy objects. Could you replace them by -1 for ex?

Gotcha! It's now not erroring on that, but rather on the call to BCE w/ logits - not sure if this is a result of a tensor not being specified as torch.float32? (https://stackoverflow.com/questions/70216222/pytorch-is-throwing-an-error-runtimeerror-result-type-float-cant-be-cast-to-th)

Stack trace:
```

RuntimeError Traceback (most recent call last)
Cell In[10], line 1
----> 1 train_results = trainer.train()

File /anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1534 self.model_wrapped = self.model
1536 inner_training_loop = find_executable_batch_size(
1537 self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
1538 )
-> 1539 return inner_training_loop(
1540 args=args,
1541 resume_from_checkpoint=resume_from_checkpoint,
1542 trial=trial,
1543 ignore_keys_for_eval=ignore_keys_for_eval,
1544 )

File /anaconda/envs/dgllife/lib/python3.8/site-packages/accelerate/utils/memory.py:136, in find_executable_batch_size..decorator(*args, **kwargs)
134 raise RuntimeError("No executable batch size found, reached zero.")
135 try:
--> 136 return function(batch_size, *args, **kwargs)
137 except Exception as e:
138 if should_reduce_batch_size(e):

File /anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/trainer.py:1809, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
...
3162 if not (target.size() == input.size()):
3163 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
-> 3165 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)

RuntimeError: result type Float can't be cast to the desired output type Long
```

Hi @seyonec ,
Yes, it's a possiblity! Can you try changing the type of the target tensors?

Modifying target in the line in functional.py to be target.float() seems to remove that issue! However, I run into an issue with the input_edge features in collating_graphormer now, weirdly.

RuntimeError: Caught RuntimeError in DataLoader worker process 1.
Original Traceback (most recent call last):
  File "/anaconda/envs/dgllife/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
  File "/anaconda/envs/dgllife/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
    return self.collate_fn(data)
  File "/anaconda/envs/dgllife/lib/python3.8/site-packages/transformers/models/graphormer/collating_graphormer.py", line 119, in __call__
    batch["input_edges"][
RuntimeError: The expanded size of the tensor (3) must match the existing size (0) at non-singleton dimension 3.  Target sizes: [1, 1, 0, 3].  Tensor sizes: [0]