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As mentioned as the title, there seems to be no interface for starting gradient accumulation.
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Hello Thanks for your great work.
I need to use gradient accumulation on batches due to RAM constraints. The training loop involves iterating over two modalities. I am concerned about the implicatio…
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There is this quote:
```
**Gradient accumulation** simulates a larger batch size than the
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252 | hardware can support and therefore does not provide any throughput
253 | benefits. It should g…
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### System Info
- `transformers` version: 4.43.0.dev0
- Platform: Linux-5.4.0-167-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- Huggingface_hub version: 0.23.4
- Safetensors version: …
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Gradient accumulation (micro step) could be very useful when we want to have large batch size but with limited number of gpus.
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Dropout is really the bane of equinox it seems. Loose follow-up of #681 - effectively, I'm trying to fix this problem that cropped up a while ago when using `optax.MultiSteps` for gradient accumulatio…
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Hello, does it support gradient accumulation or microbatches like those in the T5X repository? I didn't find a parameter for this in base.yml, maybe I just didn't see it? Thank you!
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@awaelchli I found that in the `pretrain.py`, the accumulation steps are calculated based on global batch size, device number and micro batch size.
This works fine under single-node setting, e.g. glo…
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### System Info
```Shell
I have updated to accelerate 0.31.0 from 0.30.0 and all my trainings with gradient_accumulation_steps > 1 started to collapse. Please double check that everything is ok.
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If we use the VILADistributedSampler (https://github.com/Efficient-Large-Model/VILA/blob/main/llava/train/llava_trainer.py#L274-L281) for Distributed Training, should the `gradient_accumulation_steps`…