Checkpointing for Memory Efficiency

In PyTorch, torch.utils.checkpoint reduces GPU memory use by segmenting large models during training. It stores only one segment at a time in memory, helping in training larger models or using bigger batches in limited memory. It increases epoch time but saves memory by recalculating activations during the backward pass instead of storing them.

When to use?

• Large Model Training: Ideal for scenarios where the model is too large to fit into memory during training.
  
• Memory Constraints: Useful when working with limited GPU memory resources, enabling the training of complex models without upgrading hardware.
• Long Training Processes: Applicable in situations involving extensive training periods, as it helps in managing memory usage more efficiently over time

Benefits

• Reduced Memory Footprint: Significantly lowers the GPU memory requirement by storing only part of the model at a time.
  
• Enables Larger Models: Facilitates training of models that would otherwise be too large for the available memory.

import torch
from torch import nn
from torch.utils.checkpoint import checkpoint

class LargeModel(nn.Module):
def__init__(self):
super(LargeModel, self).__init__()
# Define your model segments here
 self.part1 = nn.Linear(1000, 1000)
 self.part2 = nn.Linear(1000, 1000)

defforward(self, x):
# Use checkpointing for each part
 x = checkpoint(self.part1, x)
 x = checkpoint(self.part2, x)
return x

model = LargeModel()
input_tensor = torch.randn(10, 1000)
output = model(input_tensor)