Inquiry about Mosaic-BERT and BERT-Base Sequence Lengths

[mscherrmann]

Inquiry about Mosaic-BERT and BERT-Base Sequence Lengths

[dakinggg]

Apologies if I haven't totally understood your question.

From the blogpost:
"For all BERT-Base models, we chose the training duration to be 286,720,000 samples of sequence length 128; this covers 78.6% of C4."

To fully pretrain a model with 512 sequence length, you'll just need to follow our guide, but change the max_seq_len param to 512.

Because of alibi, you can also start with a model trained with sequence length 128, and change max_seq_len to 512 to adapt it.

[mscherrmann]

Thank you!

[mscherrmann]

Hi,

I have one follow-up question:

What do I have to consider regarding "global_train_batch_size" and "device_train_microbatch_size" if I want to train with sequence length of 512 instead of 128 tokens? If I leave everything as in the yamls/main/hf-bert-base-uncased.yaml file I probably get memory problems. Do you have any tips in this regard? Or even better: Do you have a yml for this case? I train on a Nvidia 8x80 GB A100.

Try and Error goes with me unfortunately badly, because I always have to wait quite long until I am on the GPU. Therefore the demand. Thanks a lot!

[dakinggg]

global_train_batch_size is an optimization related setting and you may or may not want to change it. If you increase the sequence length, you see more tokens per batch. device_train_microbatch_size does not affect the math, and is only related to memory. I'm not sure what setting will work on the exact setup you describe, but you can try device_train_microbatch_size=auto, which will determine it for you.

[mscherrmann]

Perfect, thank you for your quick response!

[mscherrmann]

I ran into another issue, sorry...

As mosaic-bert is not finetunable, I use the hf-bert. I follow the approach of the original BERT paper: Train 90% of the steps with a sequence length of 128 and 10% of the steps with a sequence length of 10%.

To accomplish this with your code, i run the "main" scirpt for pretraining twice. The first run completes without any issue. However, in the second run, when I load the previous checkpoint with "load_path" and change sequence length to 512, I get the following error:

ValueError: Reused local directory: ['/mnt/data/train'] vs ['/ mnt/data/train']. Provide a different one.

The data is stored locally. Do you have any idea why this error occurs?

Thank you very much!

[karan6181]

HI @FinTexIFB , what is your remote and local parameter looks like which you are passing to StreamingDataset ? Since your dataset resides locally, you can actually provide your local directory to local parameter and remote=None. For example, local='/mnt/data/train' and remote=None.

[mscherrmann]

Hi @karan6181,

thank you for your response. Yes, setting local='/mnt/data/train' and remote=None is exactly what I've done.

However, I found a workaround by simply creating a new container with the same mosaic docker image and installing all dependencies. Now it works, but only once. When I try to continue pre-training with an existing checkpoint afterwards I'll get the error. Maybe that is a bug

[jacobfulano]

@FinTexIFB, mosaic-bert is finetunable, as can be seen in this yaml. Does this work for your use case?