IndexError: pop from empty list

🐛 Bug

I am trying to replicate the idea in one paper: First only set the last fc layer in the model to be requires_grad = true and other layers are false. And then after getting the loss, using loss.backward() to get the gradients (aggregated and noise added) only for the fc layers. Then, based on those gradients, by building linear equations, to estimate the graidents of loss wrt logits, and finally using the estimated gradients to update the whole model.

I have tested the same idea in pytroch, without noise added on fc layer but solving the linear equations, and it works. But by using opacus to add noise on the fc layer, I got the error. Is this a bug or other issue? is this caused by multiple backward?

The error is caused by the line: logits. backward (dLdZ)

Please reproduce using our template Colab and post here the link

To Reproduce

:warning: We cannot help you without you sharing reproducible code. Do not ignore this part :) Steps to reproduce the behavior:

inputs, labels = data optimizer. zero_grad()

logits, inoutput = model (inputs) # inoutput is the intermediate output before the last layer of the model loss = criterion (logits, labels)

loss. backward(retain_graph=True)

fc_params = model.fc.parameters( ) grads_fc =[]

for param in fc.params: if param.grad is not None: if len (param.grad.shape) > 1: fc_grad = param.grad.view(param.grad.size(0), -1) else: fc_grad = param. grad. unsqueeze (1) grads_fc. append (fc_grad)

dLdW = torch. cat (grads_fc, dim=1) dZdB = torch.ones ( (inoutput.size(0), 1)).to(device) dZdW = torch.cat ( (inoutput, dZdB), dim=1)

A = dZdW. t() B = dLdW.t() dLdZ = torch. linalg.lstsq(A, B) .solution

logits. backward (dLdZ) optimizer.step ( )

Environment

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python collect_env.py

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pytorch / opacus