adagradw 0.0.4

AdagradW - Decoupled Weight Decay in Adagrad

adagradw - Decoupled Weight Decay for Adagrad

The Adagrad optimizer preconditions the gradient update by the accumulated sum of squares of gradients. Assuming a batch size of 1 and learning rate of 0, this accumulated sum of squares of gradients is a diagonal approximation to the Empirical Fisher information matrix. However, using weight decay, the original PyTorch implementation updates the gradient with the gradient of the regularization term before computing the squared gradient, in which case the earlier equivalence to the empirical Fisher information does not hold anymore. This implementation applies the same trick as the AdamW optimizer to decouple weight decay and learning rate in Adam, by directly applying the regularizer's gradient step rather than combining gradients, thus recovering the equivalence with approximate computation of the Empirical Fisher information even when using weight decay.

Usage

Install with pip install adagradw and then just use as drop-in replacement

- optim = torch.optim.Adagrad(model.parameters(), lr=1e-3)
+ optim = adagradw.AdagradW(model.parameters(), lr=1e-3)

  for x, y in train_dataloader:
      loss = loss_fn(model(x), y)
      
      optim.zero_grad()
      loss.backward()
      optim.step()