Training with multiple GPUs

[lfoppiano]

This PR adds the support to multi-gpu, it has been tested on multiple GPU on the same node (4 x 16Gb) allowing to provide a larger batch size.

I wanted to implement it in the trainer but the processing related to the data preparation require to to be under the with strategy.scope():.

I implemented only on the sequence labelling, once it's revised I update the classification as well.

[lfoppiano]

I've extended the support to the other scripts of the sequence labelling. Overall, I'm not sure how useful this feature is for increasing the batch_size, because the performances are not improving by increasing it on fine-tuning 😭 On the other hand it's definitely nice to have when testing big BERT models because it allow keeping the same parameters (e..g batch_size=20) that would not be possible without multi-gpu.

For example in previous of my tests, the batteryonlybert https://huggingface.co/batterydata/batteryonlybert-cased had better results, but it was due to the use of batch_size=10 to overcome the OOM happening with batch_size=20. Fine-tuning with --multi-gpu and batch_size=20 resulted in lower scores.

[kermitt2]

Thank you @lfoppiano ! I was not able to test with a multi GPU settings so just tested with normal single GPU, which is working fine as expected.

I think this is useful as you say for larger models (keeping same batch size), but also for prediction because we can increase the batch size and process more rapidly texts.

[kermitt2]

Doing more tests, training is fine but there is failure when writing a model with the -multi-gpu option, having one GPU:

python3 delft/applications/grobidTagger.py date train_eval --architecture BidLSTM_CRF --embedding glove-840B --multi-gpu

....

_________________________________________________________________
    f1 (micro): 95.78
                  precision    recall  f1-score   support

           <day>     0.9091    0.9524    0.9302        42
         <month>     0.9344    0.9661    0.9500        59
          <year>     1.0000    0.9688    0.9841        64

all (micro avg.)     0.9521    0.9636    0.9578       165

model config file saved
preprocessor save
model saved
Exception ignored in: <function Pool.__del__ at 0x7f3fa42ccb80>
Traceback (most recent call last):
  File "/usr/lib/python3.8/multiprocessing/pool.py", line 268, in __del__
    self._change_notifier.put(None)
  File "/usr/lib/python3.8/multiprocessing/queues.py", line 368, in put
    self._writer.send_bytes(obj)
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 200, in send_bytes
    self._send_bytes(m[offset:offset + size])
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 411, in _send_bytes
    self._send(header + buf)
  File "/usr/lib/python3.8/multiprocessing/connection.py", line 368, in _send
    n = write(self._handle, buf)
OSError: [Errno 9] Bad file descriptor

without -multi-gpu this error does not appear and the model is saved.

Of course, using -multi-gpu when having a single GPU might not be very consistent as a user action!

[lfoppiano]

@kermitt2 thanks for testing it. I will add the option for the inference too.

[lfoppiano]

The OSError: [Errno 9] Bad file descriptor seems due to https://github.com/tensorflow/tensorflow/issues/50487 and should be fixed in e169867f6911bb934147077beb0f1dcab4ca7a19