Fixing Unexpected Keyword Argument In ModernBert

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The forward() Method in ModernBertForTokenClassification is missing **kwargs: A Comprehensive Fix

Hey guys! Today, we're diving deep into a common issue you might encounter while fine-tuning ModernBertForTokenClassification using peft.LoraConfig(). Specifically, we're addressing the dreaded TypeError: ModernBertForTokenClassification.forward() got an unexpected keyword argument 'use_cache'. Trust me, I know how frustrating these little hiccups can be, so let's get straight to fixing it!

Understanding the Problem

So, what's really going on here? The error message tells us that the forward() method of ModernBertForTokenClassification doesn't accept the use_cache keyword argument. This typically happens when the method signature in the model definition doesn't include **kwargs to catch any unexpected keyword arguments passed to it. When you're using peft for fine-tuning, it sometimes passes additional arguments that the original model might not have explicitly defined.

To illustrate, let's break down the traceback. The error originates from peft/tuners/tuners_utils.py, where the forward method is called within the LoraModel. This method passes all arguments, including keyword arguments, to the underlying model's forward method. If ModernBertForTokenClassification.forward() doesn't accept **kwargs, it throws a TypeError. Let's examine the problematic call stack:

│ /home/gsgs2tk/ADA_ModelingFramework/.venv/lib/python3.11/site-packages/peft/tuners/tuners_utils.py:222 in forward                                                     │
│                                                                                                                                                                       │
│   219 │   │   return self.active_adapter                                                                                                                             │
│   220 │                                                                                                                                                              │
│   221 │   def forward(self, *args: Any, **kwargs: Any):                                                                                                              │
│ ❱ 222 │   │   return self.model.forward(*args, **kwargs)                                                                                                             │
│   223 │                                                                                                                                                              │

The traceback clearly shows that the LoraModel's forward method passes **kwargs to the underlying ModernBertForTokenClassification model. The kwargs dictionary contains arguments like input_ids, attention_mask, labels, and, importantly, use_cache. The absence of **kwargs in the ModernBertForTokenClassification model's forward method leads to the TypeError.

Why is this important? Well, keyword arguments provide a flexible way to pass optional parameters to a function. In the context of transformer models, arguments like use_cache, output_attentions, and output_hidden_states control the behavior of the forward pass. Without **kwargs, your model is essentially rigid, unable to adapt to these optional parameters, leading to potential compatibility issues when integrating with libraries like peft.

The Solution: Adding **kwargs

The fix is surprisingly simple. All you need to do is add **kwargs to the method signature of the forward() method in ModernBertForTokenClassification. This allows the method to accept any additional keyword arguments without raising a TypeError. Here’s how you do it:

@auto_docstring(
 custom_intro="""
 The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.
 """
)
class ModernBertForTokenClassification(ModernBertPreTrainedModel):
 def __init__(self, config: ModernBertConfig):
 super().__init__(config)
 self.num_labels = config.num_labels

 self.model = ModernBertModel(config)
 self.head = ModernBertPredictionHead(config)
 self.drop = torch.nn.Dropout(config.classifier_dropout)
 self.classifier = nn.Linear(config.hidden_size, config.num_labels)

 # Initialize weights and apply final processing
 self.post_init()

 @auto_docstring
 def forward(
 self,
 input_ids: Optional[torch.LongTensor] = None,
 attention_mask: Optional[torch.Tensor] = None,
 sliding_window_mask: Optional[torch.Tensor] = None,
 position_ids: Optional[torch.Tensor] = None,
 inputs_embeds: Optional[torch.Tensor] = None,
 labels: Optional[torch.Tensor] = None,
 indices: Optional[torch.Tensor] = None,
 cu_seqlens: Optional[torch.Tensor] = None,
 max_seqlen: Optional[int] = None,
 batch_size: Optional[int] = None,
 seq_len: Optional[int] = None,
 output_attentions: Optional[bool] = None,
 output_hidden_states: Optional[bool] = None,
 return_dict: Optional[bool] = None,
 **kwargs
# Rest of the code...

By adding **kwargs to the forward method, you ensure that any unexpected keyword arguments are gracefully accepted and can be handled appropriately within the method (or ignored if they're not needed). This simple change can save you a lot of headache when working with different libraries and configurations.

Why This Works

Let's dive a bit deeper into why adding **kwargs solves the problem. In Python, **kwargs is a special syntax that allows a function to accept an arbitrary number of keyword arguments. These arguments are passed to the function as a dictionary, where the keys are the argument names and the values are the argument values. By including **kwargs in the function signature, you're essentially telling the function to accept any keyword arguments that are passed to it, even if they're not explicitly defined in the function's parameter list.

In the context of the forward method, **kwargs acts as a catch-all for any additional keyword arguments that might be passed by the calling function. This is particularly useful when you're working with libraries like peft, which might add additional keyword arguments to the function call. Without **kwargs, the forward method would raise a TypeError if it receives any unexpected keyword arguments. By adding **kwargs, you're allowing the method to gracefully handle these additional arguments, preventing the error and allowing the program to continue execution.

Furthermore, the **kwargs parameter allows for greater flexibility and future-proofing of your code. As the Hugging Face Transformers library evolves, new keyword arguments might be added to the forward method of various models. By including **kwargs in your forward method, you ensure that your code will continue to work even if new keyword arguments are added in the future.

How to Implement the Fix

  1. Locate the ModernBertForTokenClassification class: Find the file where this class is defined in your project.
  2. Find the forward() method: Look for the forward() method within the class definition.
  3. Add **kwargs to the signature: Modify the method signature to include **kwargs. Make sure it’s the last argument in the list.
  4. Save the file: Save the changes you made to the file.
  5. Test your code: Run your code again to see if the error is resolved. It should now run without the TypeError.

Additional Tips and Considerations

  • Check Your Transformers Version: Ensure you are using a compatible version of the transformers library. The user in the original post was using version 4.57.1. While this version should generally work, keeping your libraries up-to-date can often resolve compatibility issues.

  • Inspect the kwargs Dictionary: If you're curious about what's being passed in the kwargs dictionary, you can add a print(kwargs) statement at the beginning of the forward() method. This will print the contents of the dictionary to the console, allowing you to see which keyword arguments are being passed.

  • Handle Specific Keyword Arguments: If you need to use specific keyword arguments within the forward() method, you can extract them from the kwargs dictionary. For example, if you need to use the use_cache argument, you can do something like this:

    def forward(
    self,
    input_ids: Optional[torch.LongTensor] = None,
    attention_mask: Optional[torch.Tensor] = None,
    sliding_window_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
    labels: Optional[torch.Tensor] = None,
    indices: Optional[torch.Tensor] = None,
    cu_seqlens: Optional[torch.Tensor] = None,
    max_seqlen: Optional[int] = None,
    batch_size: Optional[int] = None,
    seq_len: Optional[int] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    **kwargs
):
    use_cache = kwargs.get("use_cache", False)  # Get use_cache from kwargs, default to False
    # Rest of your code here, using the use_cache variable

    This code retrieves the value of the use_cache argument from the kwargs dictionary, defaulting to False if the argument is not present. You can then use the use_cache variable within your code.

Conclusion

So there you have it! Adding **kwargs to the forward() method of ModernBertForTokenClassification is a simple yet effective solution to the TypeError you might encounter when fine-tuning with peft.LoraConfig(). This ensures that your model can gracefully handle any unexpected keyword arguments, making it more flexible and compatible with different libraries and configurations.

Remember, these little fixes can make a big difference in your machine learning journey. Keep exploring, keep experimenting, and don't be afraid to dive deep into the code to understand what's really going on. Happy coding, and may your models always run smoothly!

By understanding the root cause of the error and applying this simple fix, you can save yourself a lot of frustration and ensure that your code runs smoothly. This is a common issue when working with transformer models and fine-tuning them with libraries like peft, so it's a valuable lesson to learn.

I hope this helps you out, and feel free to reach out if you have any more questions or run into other issues. Good luck with your fine-tuning endeavors!