[bug] support for large forward_batch_size in seq2seq models
#100
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- fix attention mask & loss issue - fix example - add attention mask construction in model
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. |
- comment calls to `_return_slice_index` function - add `offset` for `padding_side=left` support
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Closing in favour of #133. |
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What does this PR do?
First of all, please welcome the PR number 100!
Before this PR, it was not possible to run the T5 example with
forward_batch_size > 1. We suspected that something was wrong with the computation of the loss function when padding tokens are present.This PR fixes a bug we had for seq2seq models (and I believe for causalLM models too). It seems that we forgot to mask out the logits/hidden states that correspond to the pad tokens when computing the loss function.
See a similar implementation here: https://github.com/CarperAI/trlx/blob/main/trlx/trainer/nn/ppo_models.py#L166-L191 where the loss takes care of ignoring the terms corresponding to pad tokens
Can also add tests!
GPT2 run: https://wandb.ai/distill-bloom/trl/runs/4cs5z6j3?workspace=user-younesbelkada
T5 run: https://wandb.ai/distill-bloom/trl/runs/lxgi5ae9?workspace=user-younesbelkada
It seems that now the
reward_stdis higher, leading to less smoothreward_meancurves, so putting this PR as draftcc @lvwerra