Equality constraints not working in Pytorch 0.4.1

[jeffren66]

Hello.

It seems that the equality constraints are not working in the latest version.

When I run the classification experiement of OptNet at: https://github.com/locuslab/optnet/tree/master/cls with the mnist lenet --proj simproj arguments, qpth shows the following error.

I'm using qpth v0.0.13 with PyTorch 0.4.1.

Thanks.

Stack trace:

---

RuntimeError Traceback (most recent call last) in () 280 281 if name=='main': --> 282 main()

in main() 208 for epoch in range(1, args.nEpoch + 1): 209 adjust_opt(args, optimizer, epoch) --> 210 train(args, epoch, net, trainLoader, optimizer, trainF) 211 test(args, epoch, net, testLoader, optimizer, testF) 212 try:

in train(args, epoch, net, trainLoader, optimizer, trainF) 228 data, target = Variable(data), Variable(target) 229 optimizer.zero_grad() --> 230 output = net(data) 231 loss = F.nll_loss(output, target) 232 # make_graph.save('/tmp/t.dot', loss.creator); assert(False)

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 475 result = self._slow_forward(*input, **kwargs) 476 else: --> 477 result = self.forward(*input, **kwargs) 478 for hook in self._forward_hooks.values(): 479 hook_result = hook(self, input, result)

~/optnet-master/cls/models.py in forward(self, x) 47 x = F.relu(self.fc1(x)) 48 x = self.fc2(x) ---> 49 return self.projF(x) 50 51 class LenetOptNet(nn.Module):

~/optnet-master/cls/models.py in projF(x) 32 A = self.A.unsqueeze(0).expand(nBatch, 1, nCls) 33 b = self.b.unsqueeze(0).expand(nBatch, 1) ---> 34 x = QPFunction()(Q, -x.double(), G, h, A, b).float() 35 x = x.log() 36 return x

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/qpth/qp.py in forward(self, Q_, p_, G_, h_, A_, b_) 89 90 if self.solver == QPSolvers.PDIPM_BATCHED: ---> 91 self.Q_LU, self.S_LU, self.R = pdipm_b.pre_factor_kkt(Q, G, A) 92 zhats, self.nus, self.lams, self.slacks = pdipm_b.forward( 93 Q, p, G, h, A, b, self.Q_LU, self.S_LU, self.R,

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/qpth/solvers/pdipm/batch.py in pre_factor_kkt(Q, G, A) 409 410 LU_A_invQ_AT = btrifact_hack(A_invQ_AT) --> 411 P_A_invQ_AT, L_A_invQ_AT, U_A_invQ_AT = torch.btriunpack(*LU_A_invQ_AT) 412 P_A_invQ_AT = P_A_invQ_AT.type_as(A_invQ_AT) 413

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/functional.py in btriunpack(LU_data, LU_pivots, unpack_data, unpack_pivots) 121 U = LU_data.new(LU_data.size()).zero_() 122 I_diag = torch.eye(sz).type_as(LU_data).byte().unsqueeze(0).expand(nBatch, sz, sz) --> 123 L[I_diag] = 1.0 124 L[I_L] = LU_data[I_L] 125 U[I_U] = LU_data[I_U]

RuntimeError: The shape of the mask [1, 64, 64] at index 2 does not match the shape of the indexed tensor [1, 64, 1] at index 2

[jeffren66]

Changing the line 410 in qpth/solvers/pdipm/batch.py from LU_A_invQ_AT = btrifact_hack(A_invQ_AT) to LU_A_invQ_AT = [x.cuda() for x in btrifact_hack(A_invQ_AT.cpu())] seems to be suppressing the error.

[willwilliams]

I'm seeing a related error when running the same command on pytorch==1.0.1.post2 and qpth==0.0.13

File "./venv/lib/python3.6/site-packages/qpth/solvers/pdipm/batch.py", line 357, in solve_kkt invQ_rx = rx.btrisolve(*Q_LU) RuntimeError: invalid argument 3: dimensions of A and b must be equal at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:862