fix c++ interface bug (#3613)

#3578
#3579

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

source/api_cc/src/DeepPotPT.cc

@@ -3,6 +3,7 @@
 #include "DeepPotPT.h"
 
 #include "common.h"
+#include "device.h"
 using namespace deepmd;
 torch::Tensor createNlistTensor(const std::vector<std::vector<int>>& data) {
   std::vector<torch::Tensor> row_tensors;
@@ -36,15 +37,19 @@ void DeepPotPT::init(const std::string& model,
               << std::endl;
     return;
   }
-  gpu_id = gpu_rank;
-  torch::Device device(torch::kCUDA, gpu_rank);
+  int gpu_num = torch::cuda::device_count();
+  if (gpu_num > 0) {
+    gpu_id = gpu_rank % gpu_num;
+  } else {
+    gpu_id = 0;
+  }
+  torch::Device device(torch::kCUDA, gpu_id);
   gpu_enabled = torch::cuda::is_available();
   if (!gpu_enabled) {
     device = torch::Device(torch::kCPU);
-    std::cout << "load model from: " << model << " to cpu " << gpu_rank
-              << std::endl;
+    std::cout << "load model from: " << model << " to cpu " << std::endl;
   } else {
-    std::cout << "load model from: " << model << " to gpu " << gpu_rank
+    std::cout << "load model from: " << model << " to gpu " << gpu_id
               << std::endl;
   }
   module = torch::jit::load(model, device);
@@ -107,7 +112,6 @@ void DeepPotPT::compute(ENERGYVTYPE& ener,
   }
   auto int_options = torch::TensorOptions().dtype(torch::kInt64);
   auto int32_options = torch::TensorOptions().dtype(torch::kInt32);
-
   // select real atoms
   std::vector<VALUETYPE> dcoord, dforce, aparam_, datom_energy, datom_virial;
   std::vector<int> datype, fwd_map, bkw_map;
@@ -116,6 +120,25 @@ void DeepPotPT::compute(ENERGYVTYPE& ener,
   select_real_atoms_coord(dcoord, datype, aparam_, nghost_real, fwd_map,
                           bkw_map, nall_real, nloc_real, coord, atype, aparam,
                           nghost, ntypes, 1, daparam, nall, aparam_nall);
+  int nloc = nall_real - nghost_real;
+  int nframes = 1;
+  if (nloc == 0) {
+    // no backward map needed
+    ener.resize(nframes);
+    // dforce of size nall * 3
+    force.resize(static_cast<size_t>(nframes) * fwd_map.size() * 3);
+    fill(force.begin(), force.end(), (VALUETYPE)0.0);
+    // dvirial of size 9
+    virial.resize(static_cast<size_t>(nframes) * 9);
+    fill(virial.begin(), virial.end(), (VALUETYPE)0.0);
+    // datom_energy_ of size nall
+    atom_energy.resize(static_cast<size_t>(nframes) * fwd_map.size());
+    fill(atom_energy.begin(), atom_energy.end(), (VALUETYPE)0.0);
+    // datom_virial_ of size nall * 9
+    atom_virial.resize(static_cast<size_t>(nframes) * fwd_map.size() * 9);
+    fill(atom_virial.begin(), atom_virial.end(), (VALUETYPE)0.0);
+    return;
+  }
   std::vector<VALUETYPE> coord_wrapped = dcoord;
   at::Tensor coord_wrapped_Tensor =
       torch::from_blob(coord_wrapped.data(), {1, nall_real, 3}, options)
@@ -185,7 +208,6 @@ void DeepPotPT::compute(ENERGYVTYPE& ener,
   datom_virial.assign(
       cpu_atom_virial_.data_ptr<VALUETYPE>(),
       cpu_atom_virial_.data_ptr<VALUETYPE>() + cpu_atom_virial_.numel());
-  int nframes = 1;
   // bkw map
   force.resize(static_cast<size_t>(nframes) * fwd_map.size() * 3);
   atom_energy.resize(static_cast<size_t>(nframes) * fwd_map.size());
@@ -249,6 +271,24 @@ void DeepPotPT::compute(ENERGYVTYPE& ener,
     floatType = torch::kFloat32;
   }
   auto int_options = torch::TensorOptions().dtype(torch::kInt64);
+  int nframes = 1;
+  if (natoms == 0) {
+    // no backward map needed
+    ener.resize(nframes);
+    // dforce of size nall * 3
+    force.resize(static_cast<size_t>(nframes) * natoms * 3);
+    fill(force.begin(), force.end(), (VALUETYPE)0.0);
+    // dvirial of size 9
+    virial.resize(static_cast<size_t>(nframes) * 9);
+    fill(virial.begin(), virial.end(), (VALUETYPE)0.0);
+    // datom_energy_ of size nall
+    atom_energy.resize(static_cast<size_t>(nframes) * natoms);
+    fill(atom_energy.begin(), atom_energy.end(), (VALUETYPE)0.0);
+    // datom_virial_ of size nall * 9
+    atom_virial.resize(static_cast<size_t>(nframes) * natoms * 9);
+    fill(atom_virial.begin(), atom_virial.end(), (VALUETYPE)0.0);
+    return;
+  }
   std::vector<torch::jit::IValue> inputs;
   at::Tensor coord_wrapped_Tensor =
       torch::from_blob(coord_wrapped.data(), {1, natoms, 3}, options)