fix: fix loss normalization in Trainer for multi-GPU DataParallel tra…
Author: Priyanshjain10
Pull request: yes

…ining

Fixes #37474

## Problem
When training with n_gpu > 1 using DataParallel, the loss is incorrectly reduced by taking mean() instead of sum(). This causes the reported loss to be 1/n_gpu of the actual loss.

## Root Cause
DataParallel-wrapped models receive num_items_in_batch as a kwarg in Trainer.compute_loss, which is the total number of tokens across all devices. Each device computes a per-device loss that already includes division by num_items_in_batch. Taking mean() on these losses causes incorrect scaling.

## Solution
Change the loss reduction to use sum() when num_items_in_batch is provided (DataParallel case) and mean() otherwise (regular case).

## Testing
Tested with multi-GPU training to verify loss values match expected ~log(vocab_size) instead of being divided by number of GPUs.

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Fixes # (issue)


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Comments:
Closing this PR as the issue #37474 has already been comprehensively resolved in PR #40799, which was merged on September 10th. The merged solution uses a more robust `_reduce_loss` method that properly handles loss aggregation across single and multi-GPU setups. Please refer to that PR for the complete fix.