How do you implement gradient checkpointing to manage memory during large model training

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With the help of python programming implement gradient Checkpointing to manage memory during large model training?
Nov 8 in Generative AI by Ashutosh
• 6,050 points
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1 answer to this question.

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In order to implement gradient checkpointing to manage memory during large model training then refer to the code snippet below:

In this code, gradient checkpointing is implemented by  "checkpoint" on "self.layer1" and "self.layer2" in the forward method. This means that during the forward pass, the intermediate activities for this layer are not stored, saving memory,Recomputed during backpropogation to calcaulate the gradient.

Hence, using checkpoints on selected layers will help in saving memory during large model training.

answered Nov 8 by anonymous

edited Nov 11 by Ashutosh

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