Repository: huggingface/transformers
Pull request: Fixed a bug calculating cross entropy loss in `JetMoeForCausalLM`

In the original code, we shift the logits and pass `shift_logits` into the `self.loss_function`, but in `self.loss_function`, the `shift_logits` will be shifted again, so we are actually doing **next next token prediction**, which is incorrect. I have removed the logits shifting before calling `self.loss_function`.

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Fixed incorrect cross entropy calculation in `JetMoeForCausalLM`. 

The original code snippet of the model's forward function is:
```python
if labels is not None:

  logits = logits.float()
  # NOTICE: The labels are shifted once here
  shift_logits = logits[..., :-1, :].contiguous()
  shift_labels = labels[..., 1:].contiguous()

  shift_logits = shift_logits.view(-1, self.config.vocab_size)
  shift_labels = shift_labels.view(-1)

  shift_labels = shift_labels.to(shift_logits.device)
  loss = self.loss_function(
      shift_logits,
      shift_labels,
      vocab_size=self.config.vocab_size,
      **kwargs,
  )
```
but in `self.loss_function`, typically `ForCausalLMLoss`:
```python
def ForCausalLMLoss(
    logits,
    labels,
    vocab_size: int,
    num_items_in_batch: Optional[int] = None,
    ignore_index: int = -100,
    shift_labels: Optional[torch.Tensor] = None,
    **kwargs,
) -> torch.Tensor:
    logits = logits.float()
   # NOTICE: In the above call, the shift_labels is None (we pass shift_labels as labels, not shift_labels), so the labels will be shifted again!
    if shift_labels is None:
        labels = nn.functional.pad(labels, (0, 1), value=ignore_index)
        shift_labels = labels[..., 1:].contiguous()

    logits = logits.view(-1, vocab_size)
    shift_labels = shift_labels.view(-1)

    shift_labels = shift_labels.to(logits.device)
    loss = fixed_cross_entropy(logits, shift_labels, num_items_in_batch, ignore_index, **kwargs)
    return loss
```

So we are doing **next next token prediction**, which is incorrect. We need to remove the first label shifting operation to fix this bug, that's what I do in this PR. With the modified code snippet, I can get reasonable results in SFT tasks.

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Changed files:
filename: src/transformers/models/jetmoe/modeling_jetmoe.py
patch: @@ -1205,19 +1205,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            # Upcast to float if we need to compute the loss to avoid potential precision issues
-            logits = logits.float()
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Ensure tensors are on the same device
-            shift_labels = shift_labels.to(shift_logits.device)
             loss = self.loss_function(
-                shift_logits,
-                shift_labels,
+                logits,
+                labels,
                 vocab_size=self.config.vocab_size,
                 **kwargs,
             )