Reducing GPU utilization while working with CLAREAugmenter

[dkoterwa]

Hi,
I was trying to use CLAREAugmenter from your library and was surprised that it consumes more than 40GB of VRAM during the augmentation process.

I have some longer texts (~800 tokens) but did not expect that a method loading a simple distilroberta underneath will cause any memory issues.

Is there any way to reduce the memory consumption in this augmenter?

The code that I am using is really simple:

from textattack.augmentation.recipes import CLAREAugmenter
augmenter = CLAREAugmenter(pct_words_to_swap=0.1)
augmented_texts= []
for text in texts:
    augmented_texts.append(augmenter.augment(text))

[tsalmonking]

I found that what causes this is the fact that recipes like CLARE that use TensorFlow end up allocating the entire memory when not using parallelization, as memory growth is only set then.
Simply applying this before loading the augmentation should fix it:

TextAttack/textattack/attacker.py

Line 551 in 0d0929c

tf.config.experimental.set_memory_growth(gpu, True)