Handles gpu/cpu transfer in QPFunction's backward + replaces torch.Te…

…nsor by torch.empty or torch.tensor

bindings/python/proxsuite/torch/qplayer.py

@@ -114,9 +114,9 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
             if ctx.cpu is not None:
                 ctx.cpu = max(1, int(ctx.cpu / 2))
 
-            zhats = torch.Tensor(nBatch, ctx.nz).type_as(Q)
-            lams = torch.Tensor(nBatch, ctx.neq).type_as(Q)
-            nus = torch.Tensor(nBatch, ctx.nineq).type_as(Q)
+            zhats = torch.empty((nBatch, ctx.nz)).type_as(Q)
+            lams = torch.empty((nBatch, ctx.neq)).type_as(Q)
+            nus = torch.empty((nBatch, ctx.nineq)).type_as(Q)
 
             for i in range(nBatch):
                 qp = ctx.vector_of_qps.init_qp_in_place(ctx.nz, ctx.neq, ctx.nineq)
@@ -163,22 +163,23 @@ def forward(ctx, Q_, p_, A_, b_, G_, l_, u_):
                     ctx.vector_of_qps.get(i).solve()
 
             for i in range(nBatch):
-                zhats[i] = torch.Tensor(ctx.vector_of_qps.get(i).results.x)
-                lams[i] = torch.Tensor(ctx.vector_of_qps.get(i).results.y)
-                nus[i] = torch.Tensor(ctx.vector_of_qps.get(i).results.z)
+                zhats[i] = torch.tensor(ctx.vector_of_qps.get(i).results.x)
+                lams[i] = torch.tensor(ctx.vector_of_qps.get(i).results.y)
+                nus[i] = torch.tensor(ctx.vector_of_qps.get(i).results.z)
 
             return zhats, lams, nus
 
         @staticmethod
         def backward(ctx, dl_dzhat, dl_dlams, dl_dnus):
+            device = dl_dzhat.device
             nBatch, dim, neq, nineq = ctx.nBatch, ctx.nz, ctx.neq, ctx.nineq
-            dQs = torch.Tensor(nBatch, ctx.nz, ctx.nz)
-            dps = torch.Tensor(nBatch, ctx.nz)
-            dGs = torch.Tensor(nBatch, ctx.nineq, ctx.nz)
-            dus = torch.Tensor(nBatch, ctx.nineq)
-            dls = torch.Tensor(nBatch, ctx.nineq)
-            dAs = torch.Tensor(nBatch, ctx.neq, ctx.nz)
-            dbs = torch.Tensor(nBatch, ctx.neq)
+            dQs = torch.empty(nBatch, ctx.nz, ctx.nz, device=device)
+            dps = torch.empty(nBatch, ctx.nz, device=device)
+            dGs = torch.empty(nBatch, ctx.nineq, ctx.nz, device=device)
+            dus = torch.empty(nBatch, ctx.nineq, device=device)
+            dls = torch.empty(nBatch, ctx.nineq, device=device)
+            dAs = torch.empty(nBatch, ctx.neq, ctx.nz, device=device)
+            dbs = torch.empty(nBatch, ctx.neq, device=device)
 
             ctx.cpu = os.cpu_count()
             if ctx.cpu is not None:
@@ -211,11 +212,11 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus):
             else:
                 for i in range(nBatch):
                     rhs = np.zeros(n_tot)
-                    rhs[:dim] = dl_dzhat[i]
+                    rhs[:dim] = dl_dzhat[i].cpu()
                     if dl_dlams != None:
-                        rhs[dim : dim + neq] = dl_dlams[i]
+                        rhs[dim : dim + neq] = dl_dlams[i].cpu()
                     if dl_dnus != None:
-                        rhs[dim + neq :] = dl_dnus[i]
+                        rhs[dim + neq :] = dl_dnus[i].cpu()
                     qpi = ctx.vector_of_qps.get(i)
                     proxsuite.proxqp.dense.compute_backward(
                         qp=qpi,
@@ -226,25 +227,25 @@ def backward(ctx, dl_dzhat, dl_dlams, dl_dnus):
                     )
 
             for i in range(nBatch):
-                dQs[i] = torch.Tensor(
+                dQs[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_dH
                 )
-                dps[i] = torch.Tensor(
+                dps[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_dg
                 )
-                dGs[i] = torch.Tensor(
+                dGs[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_dC
                 )
-                dus[i] = torch.Tensor(
+                dus[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_du
                 )
-                dls[i] = torch.Tensor(
+                dls[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_dl
                 )
-                dAs[i] = torch.Tensor(
+                dAs[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_dA
                 )
-                dbs[i] = torch.Tensor(
+                dbs[i] = torch.tensor(
                     ctx.vector_of_qps.get(i).model.backward_data.dL_db
                 )