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rafaelvalle avatar rafaelvalle commented on May 23, 2024 1

Yes! It's worth trying and can lead to lower validation losses.
Let us know what your findings are.

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shahuzi avatar shahuzi commented on May 23, 2024

Yes! It's worth trying and can lead to lower validation losses.
Let us know what your findings are.

Is there any prior guidance for setting batch_size to 1?
I found that for my data, batch_size can be set to 16.

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srcj avatar srcj commented on May 23, 2024

@IsakWesterlundBitville Thanks for your comment, I tried your suggestion to compare training with batch size 4 against default batch size 1 when using 2 GPUs. However, when I use batch size 4, I just get the the diagonal line of the alignment till a much smaller decoder time step compared with the alignment obtained with batch size 1. Please could you tell if you changed any other config settings to take into account the change in batch size.
alignment_weights0_1flow
attention_weights_0_step45K

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srcj avatar srcj commented on May 23, 2024

@IsakWesterlundBitville Thanks for your comment, I tried your suggestion to compare training with batch size 4 against default batch size 1 when using 2 GPUs. However, when I use batch size 4, I just get the the diagonal line of the alignment till a much smaller decoder time step compared with the alignment obtained with batch size 1. Please could you tell if you changed any other config settings to take into account the change in batch size.
alignment_weights0_1flow
attention_weights_0_step45K

@rafaelvalle Do you have any idea why I get this difference in the alignment plots?

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rafaelvalle avatar rafaelvalle commented on May 23, 2024

The two samples are different, hence the alignment plots are different.

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