Make use of PyTorch's type promotion logic to reduce memory access (#446)

PyTorch has supported type promotion since the last PTDC. We shouldn't manually convert the dtype now.
Co-authored-by: Farhad Ramezanghorbani <farhadrgh@users.noreply.github.com>

torchani/utils.py

@@ -135,7 +135,7 @@ def map2central(cell, coordinates, pbc):
     inv_cell = torch.inverse(cell)
     coordinates_cell = torch.matmul(coordinates, inv_cell)
     # Step 2: wrap cell coordinates into [0, 1)
-    coordinates_cell -= coordinates_cell.floor() * pbc.to(coordinates_cell.dtype)
+    coordinates_cell -= coordinates_cell.floor() * pbc
     # Step 3: convert from cell coordinates back to standard cartesian
     # coordinate
     return torch.matmul(coordinates_cell, cell)
@@ -191,8 +191,8 @@ class EnergyShifter(torch.nn.Module):
         """(species, molecular energies)->(species, molecular energies + sae)
         """
         species, energies = species_energies
-        sae = self.sae(species).to(energies.device)
-        return SpeciesEnergies(species, energies.to(sae.dtype) + sae)
+        sae = self.sae(species)
+        return SpeciesEnergies(species, energies + sae)
 
 
 class ChemicalSymbolsToInts: