Comments (2)
PiotrNawrot
commented on September 14, 2024
1
@Taytay Thanks for the nice comments, I'm glad you like the repo! Please accept my apologies for the late reply. I've been very busy lately with the ICML submission.
Yes, exactly. FA didn't support back propagation through the extra additive bias (after dot-products, before softmax). I've just noticed this PR, it looks great - I'm sure that backprop through these bias would help not only in the T5 case! Can't wait to have it merged to FA. I'll defo test it soon after it : ).
Closing for now
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harish-kamath
commented on September 14, 2024
Someone has started a repo, based off of this one, with FA2 support
@catie-aq
https://github.com/catie-aq/flashT5
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Related Issues (20)
- RMS scaling issues HOT 15
- pre-train on long context. HOT 1
- How to run on CPU HOT 1
- Difficulty applying NanoT5 to different model and database HOT 2
- AttributeError: Can't pickle local object 'IterableDataset.map.<locals>.<lambda>' HOT 1
- About pre-training on another dataset HOT 7
- Pre-training fails at step 30155 out of 32768 steps every time HOT 7
- self-defined loss function failed to work (torch._dynamo.exc.InternalTorchDynamoError: ln_encoder) HOT 4
- nanoT5 initializes lm_head weights with 768x too much variance, probably HOT 19
- Transformation to HF model
- Pre-train on different Dataset than C4 HOT 1
- Larger models and training on the Pile HOT 5
- How to create pytorch_model.bin file? HOT 1
- Silly question: Why do you need to re-implement T5 model? HOT 3
- Question about implementing whole word masking in nanoT5 HOT 1
- Beginner Question : Would it be wise to use this as a backbone for custom seq2seq modeling fMRI data and custom encoder? HOT 2
- Learning rate for multi-GPUs training HOT 3
- Just a quick question to pretrain Flan-T5 HOT 5
- Continued pretraining from official models. HOT 1
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