Graphormer multi label classification label input format
### System Info

NA

### Who can help?

@clefourrier

Kindly share the input format for multi label classification specially on the label side.

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

NA

### Expected behavior

NA
Hi!

It's basically a list of ints.

You can see an example of a graph with multiple labels with the [ogbg-molcpba dataset](https://huggingface.co/datasets/OGB/ogbg-molpcba). There is a detailed explanation of the types needed as inputs of graph classification in the  [blog post on graph classification](https://huggingface.co/blog/graphml-classification) using transformers.

Can you please tell me what added information you need? <|||||>> Hi!
> 
> It's basically a list of ints.
> 
> You can see an example of a graph with multiple labels with the [ogbg-molcpba dataset](https://huggingface.co/datasets/OGB/ogbg-molpcba). There is a detailed explanation of the types needed as inputs of graph classification in the [blog post on graph classification](https://huggingface.co/blog/graphml-classification) using transformers.
> 
> Can you please tell me what added information you need?



While trying to train, I'm getting the error message of 

TypeError: _stack_dispatcher() got an unexpected keyword argument 'dim'.

At the same time, It is working for regression/Binary classification/multi class classification usecases.<|||||>Hi!
Could you provide your full stack trace please?<|||||>> st of ints.


Please find below stack trace:

/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 1>:1                                                                              │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1664 in train                    │
│                                                                                                  │
│   1661 │   │   inner_training_loop = find_executable_batch_size(                                 │
│   1662 │   │   │   self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size  │
│   1663 │   │   )                                                                                 │
│ ❱ 1664 │   │   return inner_training_loop(                                                       │
│   1665 │   │   │   args=args,                                                                    │
│   1666 │   │   │   resume_from_checkpoint=resume_from_checkpoint,                                │
│   1667 │   │   │   trial=trial,                                                                  │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1909 in _inner_training_loop     │
│                                                                                                  │
│   1906 │   │   │   │   rng_to_sync = True                                                        │
│   1907 │   │   │                                                                                 │
│   1908 │   │   │   step = -1                                                                     │
│ ❱ 1909 │   │   │   for step, inputs in enumerate(epoch_iterator):                                │
│   1910 │   │   │   │   total_batched_samples += 1                                                │
│   1911 │   │   │   │   if rng_to_sync:                                                           │
│   1912 │   │   │   │   │   self._load_rng_state(resume_from_checkpoint)                          │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:633 in __next__           │
│                                                                                                  │
│    630 │   │   │   if self._sampler_iter is None:                                                │
│    631 │   │   │   │   # TODO(https://github.com/pytorch/pytorch/issues/76750)                   │
│    632 │   │   │   │   self._reset()  # type: ignore[call-arg]                                   │
│ ❱  633 │   │   │   data = self._next_data()                                                      │
│    634 │   │   │   self._num_yielded += 1                                                        │
│    635 │   │   │   if self._dataset_kind == _DatasetKind.Iterable and \                          │
│    636 │   │   │   │   │   self._IterableDataset_len_called is not None and \                    │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:677 in _next_data         │
│                                                                                                  │
│    674 │                                                                                         │
│    675 │   def _next_data(self):                                                                 │
│    676 │   │   index = self._next_index()  # may raise StopIteration                             │
│ ❱  677 │   │   data = self._dataset_fetcher.fetch(index)  # may raise StopIteration              │
│    678 │   │   if self._pin_memory:                                                              │
│    679 │   │   │   data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)            │
│    680 │   │   return data                                                                       │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py:54 in fetch             │
│                                                                                                  │
│   51 │   │   │   │   data = [self.dataset[idx] for idx in possibly_batched_index]                │
│   52 │   │   else:                                                                               │
│   53 │   │   │   data = self.dataset[possibly_batched_index]                                     │
│ ❱ 54 │   │   return self.collate_fn(data)                                                        │
│   55                                                                                             │
│                                                                                                  │
│ /usr/local/lib/python3.10/dist-packages/transformers/models/graphormer/collating_graphormer.py:1 │
│ 32 in __call__                                                                                   │
│                                                                                                  │
│   129 │   │   │   else:  # binary classification                                                 │
│   130 │   │   │   │   batch["labels"] = torch.from_numpy(np.concatenate([i["labels"] for i in    │
│   131 │   │   else:  # multi task classification, left to float to keep the NaNs                 │
│ ❱ 132 │   │   │   batch["labels"] = torch.from_numpy(np.stack([i["labels"] for i in features],   │
│   133 │   │                                                                                      │
│   134 │   │   return batch                                                                       │
│   135                                                                                            │
│ in stack:179                                                                                     │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: _stack_dispatcher() got an unexpected keyword argument 'dim'<|||||>Hi @techthiyanes ,
Could you please provide the command you launched or a code snippet so I can make sure I'm working on the same thing as you?<|||||>Hi @clefourrier ,

Thank you for your time and response.
Please find below code snippet that i have tried where num_classes are not passed inside arguments as it's multi label classification.

# -*- coding: utf-8 -*-
"""Untitled334.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Xnz4vI75fkIdQVT6wKKiuDipoQzO4uZ1
"""

!pip install -q -U datasets transformers Cython accelerate

!pip install -q -U matplotlib networkx

from transformers.utils import is_cython_available
print("Cython is installed:", is_cython_available())

from datasets import load_dataset 
dataset = load_dataset("OGB/ogbg-molpcba")
dataset['train'] = dataset['train'].select(list(range(1000)))
dataset['test'] = dataset['test'].select(list(range(100)))
dataset['validation'] = dataset['validation'].select(list(range(100)))
from datasets import load_metric
metric = load_metric("accuracy")
import networkx as nx
import matplotlib.pyplot as plt
# We want to plot the first train graph
graph = dataset["train"][0]
edges = graph["edge_index"]
num_edges = len(edges[0])
num_nodes = graph["num_nodes"]
# Conversion to networkx format
G = nx.Graph()
G.add_nodes_from(range(num_nodes))
G.add_edges_from([(edges[0][i], edges[1][i]) for i in range(num_edges)])
# Plot
nx.draw(G)

dataset

from transformers.models.graphormer.collating_graphormer import preprocess_item, GraphormerDataCollator
dataset_processed = dataset.map(preprocess_item, batched=False)
# split up training into training + validation
train_ds = dataset_processed['train']
val_ds = dataset_processed['validation']

from transformers import GraphormerForGraphClassification

model_checkpoint = "clefourrier/graphormer-base-pcqm4mv2" # pre-trained model from which to fine-tune

model = GraphormerForGraphClassification.from_pretrained(
    model_checkpoint, 
    # num_classes=2,  Commenting due to multi label
    ignore_mismatched_sizes = True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
)

from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
    "graph-classification",
    logging_dir="graph-classification",
    per_device_train_batch_size=64,
    per_device_eval_batch_size=64,
    auto_find_batch_size=True, # batch size can be changed automatically to prevent OOMs
    gradient_accumulation_steps=10,
    dataloader_num_workers=4, 
    num_train_epochs=20,
    evaluation_strategy="epoch",
    logging_strategy="epoch",
    # push_to_hub=False,
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    data_collator=GraphormerDataCollator()
)

trainer.train()

!pip install -q -U datasets transformers Cython accelerate

!pip install -q -U matplotlib networkx

from transformers.utils import is_cython_available
print("Cython is installed:", is_cython_available())

from datasets import load_dataset 
dataset = load_dataset("OGB/ogbg-molpcba")
dataset['train'] = dataset['train'].select(list(range(1000)))
dataset['test'] = dataset['test'].select(list(range(100)))
dataset['validation'] = dataset['validation'].select(list(range(100)))
from datasets import load_metric
metric = load_metric("accuracy")
import networkx as nx
import matplotlib.pyplot as plt
# We want to plot the first train graph
graph = dataset["train"][0]
edges = graph["edge_index"]
num_edges = len(edges[0])
num_nodes = graph["num_nodes"]
# Conversion to networkx format
G = nx.Graph()
G.add_nodes_from(range(num_nodes))
G.add_edges_from([(edges[0][i], edges[1][i]) for i in range(num_edges)])
# Plot
nx.draw(G)

dataset

from transformers.models.graphormer.collating_graphormer import preprocess_item, GraphormerDataCollator
dataset_processed = dataset.map(preprocess_item, batched=False)
# split up training into training + validation
train_ds = dataset_processed['train']
val_ds = dataset_processed['validation']

from transformers import GraphormerForGraphClassification

model_checkpoint = "clefourrier/graphormer-base-pcqm4mv2" # pre-trained model from which to fine-tune

model = GraphormerForGraphClassification.from_pretrained(
    model_checkpoint, 
    # num_classes=2,  Commenting due to multi label
    ignore_mismatched_sizes = True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
)

from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(
    "graph-classification",
    logging_dir="graph-classification",
    per_device_train_batch_size=64,
    per_device_eval_batch_size=64,
    auto_find_batch_size=True, # batch size can be changed automatically to prevent OOMs
    gradient_accumulation_steps=10,
    dataloader_num_workers=4, 
    num_train_epochs=20,
    evaluation_strategy="epoch",
    logging_strategy="epoch",
    # push_to_hub=False,
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    data_collator=GraphormerDataCollator()
)

trainer.train()

Thanks
Thiya<|||||>Ok, thank you very much for reporting! 

I can reproduce your issue, I'll fix it asap<|||||>I fixed this problem in the PR above (now we need to wait for the fix to be merged, which will not be instantaneous). Thank you very much for reporting! :hugs: 

Note that for multi-label classification, you will also need to provide the correct number of labels (in this case 128) to `num_classes`, like so:
```python
model_checkpoint = "clefourrier/graphormer-base-pcqm4mv2" # pre-trained model from which to fine-tune

model = GraphormerForGraphClassification.from_pretrained(
    model_checkpoint,
    num_classes=128, # HERE
    ignore_mismatched_sizes = True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
)
```