Optimize Fprotein batch memory usage

SFC_Torch/Fmodel.py

@@ -978,7 +978,8 @@ def calc_fprotein_batch(self, atoms_position_batch, Return=False, PARTITION=20):
 
         PARTITION: Int, default 20
             To reduce the memory cost during the computation, we divide the batch into several partitions and loops through them.
-            Larger PARTITION will require larger GPU memory. Default 20 will take around 4GB, if N_atoms~1600 and N_HKLs~13000.
+            Larger PARTITION will require larger GPU memory. Default 20 will require around 21GB, if N_atoms~2400 and N_HKLs~24000. 
+            Memory scales linearly with PARITION, N_atoms, and N_HKLs.
             But larger PARTITION will give a smaller wall time, so this is a trade-off.
         """
         # Read and tensor-fy necessary information
@@ -1253,7 +1254,7 @@ def F_protein_batch(
     # F_calc = sum_Gsum_j{ [f0_sj*DWF*exp(2*pi*i*(h,k,l)*(R_G*(x1,x2,x3)+T_G))]} fractional postion, Rupp's Book P279
     # G is symmetry operations of the spacegroup and j is the atoms
     # DWF is the Debye-Waller Factor, has isotropic and anisotropic version, based on the PDB file input, Rupp's Book P641
-    HKL_tensor = torch.tensor(HKL_array, device=try_gpu()).type(torch.float32)
+    HKL_tensor = torch.tensor(HKL_array).to(fullsf_tensor)
     batchsize = atom_pos_frac_batch.shape[0]
 
     oc_sf = fullsf_tensor * atom_occ[..., None]  # [N_atom, N_HKLs]
@@ -1282,25 +1283,28 @@ def F_protein_batch(
         start = j * PARTITION
         end = min((j + 1) * PARTITION, batchsize)
         for i in range(N_ops):  # Loop through symmetry operations to reduce memory cost
+            # [N_atoms, N_HKLs]
+            dwf_aniso = DWF_aniso(
+                atom_aniso_uw, orth2frac_tensor, sym_oped_hkl[:, i, :]
+            )  
+            # [N_atoms, N_HKLs]
+            dwf_all = torch.where(
+                mask_vec[:, None], dwf_iso, dwf_aniso
+            ) 
             # Shape [PARTITION, N_atoms, N_HKLs]
-            phase_ij = (
+            exp_phase_ij = dwf_all * torch.exp(1j * (
                 2
                 * torch.pi
                 * torch.tensordot(
                     sym_oped_pos_frac[start:end, :, :, i], HKL_tensor.T, 1
                 )
-            )
-            dwf_aniso = DWF_aniso(
-                atom_aniso_uw, orth2frac_tensor, sym_oped_hkl[:, i, :]
-            )  # [N_atoms, N_HKLs]
-            dwf_all = torch.where(
-                mask_vec[:, None], dwf_iso, dwf_aniso
-            )  # [N_atoms, N_HKLs]
-            exp_phase_ij = dwf_all * torch.exp(1j * phase_ij)
+            ))
             # Shape [PARTITION, N_HKLs], sum over atoms
             Fcalc_ij = torch.sum(exp_phase_ij * oc_sf, dim=1)
+            del exp_phase_ij # release the memory
             # Shape [PARTITION, N_HKLs], sum over symmetry operations
             Fcalc_j = Fcalc_j + Fcalc_ij
+            del Fcalc_ij
         if j == 0:
             F_calc = Fcalc_j
         else:

SFC_Torch/utils.py

@@ -107,7 +107,7 @@ def DWF_iso(b_iso, dr2_array):
     -------
     A 2D [N_atoms, N_HKLs] float32 tensor with DWF corresponding to different atoms and different HKLs
     """
-    dr2_tensor = torch.tensor(dr2_array, device=try_gpu())
+    dr2_tensor = torch.tensor(dr2_array).to(b_iso)
     return torch.exp(-b_iso.view([-1, 1]) * dr2_tensor / 4.0).type(torch.float32)