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View Code? Open in Web Editor NEWA Framework aims to wisely initialize unseen subword embeddings in PLMs for efficient large-scale continued pretraining
A Framework aims to wisely initialize unseen subword embeddings in PLMs for efficient large-scale continued pretraining
Hi there!
Very cool work :)
One thing I wanted to point out is that your work does not correctly compare throughput of variants in Table 3.
Huggingface transformers (and your implementation) unnecessarily forwards tokens with invalid labels (i.e., tokens for which labels = -100
) through the language modelling head during training training.
Consequently, your sizable vocabulary (~400K) significantly slows down variants with higher dimensional embedding matrices as a lot of unrequired dot products are computed.
As a fix, at training time your implementation should sub-select tokens with valid labels prior to the forward pass in the LMHead
.
Some back of the envelope math:
batch_size * seq_len * 0.8 * 401000 = 62914M
unrequired inner products.. per batch I'm afraid. I don't think I'm missing something, but of course please double check :)
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