Freezing Individual Weights In Pytorch

The following question is not a duplicate of How to apply layer-wise learning rate in Pytorch? because this question aims at freezing a subset of a tensor from training rather than

Solution 1:

What you have seems like it would work provided you did it after loss.backward() and before optimizer.step() (referring to the common usage for these variable names). That said, it seems a bit convoluted. Also, if your weights are floating point values then comparing them to exactly zero is probably a bad idea, we could introduce an epsilon to account for this. IMO the following is a little cleaner than the solution you proposed:

# locate zero-value weights before training loop
EPS = 1e-6
locked_masks = {n: torch.abs(w) < EPS for n, w in model.named_parameters() if n.endswith('weight')}

...

for ... #training loop

    ...

    optimizer.zero_grad()
    loss.backward()
    # zero the gradients of interest
    for n, w in model.named_parameters():                                                                                                                                                                           
        if w.grad is not None and n in locked_masks:                                                                                                                                                                                   
            w.grad[locked_masks[n]] = 0 
    optimizer.step()