4.47.1 Hugging Face Trainer loss accumulated by sum instead of mean

[jdf-prog]

System Info

Version: transformers==4.47.1

When I training model using this version of hugging face trainer, the loss is accumulated by sum instead of mean. Or it's better saying that the tr_loss_step did not divide by the global batch size. That means, the reported loss will scale proportionally with the global batch size.

When I change the transformer version to be 4.44.0, this problem is and everything works good.

My global batch size is set to 128 here. You can see from the above image that tr_loss_step=original_loss / 128, which is not the case when transformers version is 4.47.1, where tr_loss_step=original_loss.

Who can help?

No response

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

1. Any training with transformers==4.47.1

Expected behavior

loss accumulated by sum instead of mean

[Rocketknight1]

cc @SunMarc @muellerzr

[SunMarc]

Can you try with the main branch of transformers, we fixed a couple of issue grad accumulation, so your issue might be solved. Otherwise, can you share a minimal reproducer ? Thanks !

[jdf-prog]

Can you try with the main branch of transformers, we fixed a couple of issue grad accumulation, so your issue might be solved. Otherwise, can you share a minimal reproducer ? Thanks !

A simple reproduce would be using llamafactory:

git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e .
llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml

And test for transformers==4.47.1 and transformers<=4.45.2, where the former should has higher loss than the later.

However, I cannot test transformers==4.48.0 on the main branch since there seems a lot of change in the modeling_llama file.

[fan-yang1]

I also encountered the same problem in this version, when adjusting gradient_accumulation_steps, the loss scales accordingly