Add support for passing args and kwargs to per-sample loss functions

[david-stan]

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Docs change / refactoring / dependency upgrade

Motivation and Context / Related issue

It prevents TypeError: DPLossFastGradientAdaptiveClipping.__call__() got an unexpected keyword argument 'vocab_size' error from triggering when assigning DPLossFastGradientAdaptiveClipping or DPLossFastGradientClipping to the .loss_function property of any PreTrainedModel.

Every PreTrainedModel.loss_function() call expects vocab_size amongst it's keyword arguments:

# transformers.models.gpt2.modeling_gpt2.py:1099
# Flatten the tokens
loss = self.loss_function(
        logits,
        labels,
        vocab_size=self.config.vocab_size,
        **kwargs,
    )

Meanwhile, DPLossFastGradientAdaptiveClipping.__call__ and DPLossFastGradientClipping.__call__ don't have this keyword argument vocab_size in their signature. vocab size is later needed for tensor flattening:

def ForCausalLMLoss(
    logits,
    labels,
    vocab_size: int,
    num_items_in_batch: Optional[torch.Tensor] = None,
    ignore_index: int = -100,
    shift_labels: Optional[torch.Tensor] = None,
    **kwargs,
) -> torch.Tensor:
    # Upcast to float if we need to compute the loss to avoid potential precision issues
    logits = logits.float()

    if shift_labels is None:
        # Shift so that tokens < n predict n
        labels = nn.functional.pad(labels, (0, 1), value=ignore_index)
        shift_labels = labels[..., 1:].contiguous()

    # Flatten the tokens
    logits = logits.view(-1, vocab_size) <------ used here

How Has This Been Tested (if it applies)

Tested and trained on transformers' GPT2LMHeadModel with LoRA and 4B parameter Llama LlamaForCausalLM model, purposefully targeting different model architectures.

Checklist

• The documentation is up-to-date with the changes I made.
• I have read the CONTRIBUTING document and completed the CLA (see CONTRIBUTING).
• All tests passed, and additional code has been covered with new tests.

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[iden-kalemaj]

Review automatically exported from Phabricator review in Meta.

[facebook-github-bot]

@aparna-aketi merged this pull request in c9032e9.