print sparsity llevels before deleting elements

graphcore-research · Oct 10, 2023 · cd2b2d5 · cd2b2d5
1 parent 5d1e67e
commit cd2b2d5
Showing 1 changed file with 17 additions and 18 deletions.
diff --git a/pyscf_ipu/nanoDFT/nanoDFT.py b/pyscf_ipu/nanoDFT/nanoDFT.py
@@ -87,21 +87,21 @@ def exchange_correlation(density_matrix, grid_AO, grid_weights):
     """Compute exchange correlation integral using atomic orbitals (AO) evalauted on a grid. """
     # Perfectly SIMD parallelizable over grid_size axis.
     # Only need one reduce_sum in the end. 
-    grid_AO_dm = grid_AO[0] @ density_matrix # (gsize, N) @ (N, N) -> (gsize, N)
-    grid_AO_dm = jnp.expand_dims(grid_AO_dm, axis=0) # (1, gsize, N)
+    grid_AO_dm = grid_AO[0] @ density_matrix                                                    # (gsize, N) @ (N, N) -> (gsize, N)
+    grid_AO_dm = jnp.expand_dims(grid_AO_dm, axis=0)                                            # (1, gsize, N)
     mult = grid_AO_dm * grid_AO  
-    rho = jnp.sum(mult, axis=2) # (4, grid_size)=(4, 45624) for C6H6.
-    E_xc, vrho, vgamma = b3lyp(rho, EPSILON_B3LYP)                                               # (gridsize,) (gridsize,) (gridsize,)
-    E_xc = jax.lax.psum(jnp.sum(rho[0] * grid_weights * E_xc), axis_name="p")                                                 # float=-27.968[Ha] for C6H6 at convergence.
-    rho = jnp.concatenate([vrho.reshape(1, -1)/2, 4*vgamma*rho[1:4]], axis=0) * grid_weights     # (4, grid_size)=(4, 45624)
-    grid_AO_T = grid_AO[0].T # (N, gsize)
-    rho = jnp.expand_dims(rho, axis=2) # (4, gsize, 1)
-    grid_AO_rho = grid_AO * rho # (4, gsize, N)
-    sum_grid_AO_rho = jnp.sum(grid_AO_rho, axis=0) # (gsize, N)
-    V_xc = grid_AO_T @ sum_grid_AO_rho # (N, N)
-    V_xc = jax.lax.psum(V_xc, axis_name="p") #(N, N)
-    V_xc = V_xc + V_xc.T                                                                         # (N, N)
-    return E_xc, V_xc                                                                            # (float) (N, N)
+    rho = jnp.sum(mult, axis=2)                                                                 # (4, grid_size)=(4, 45624) for C6H6.
+    E_xc, vrho, vgamma = b3lyp(rho, EPSILON_B3LYP)                                              # (gridsize,) (gridsize,) (gridsize,)
+    E_xc = jax.lax.psum(jnp.sum(rho[0] * grid_weights * E_xc), axis_name="p")                   # float=-27.968[Ha] for C6H6 at convergence.
+    rho = jnp.concatenate([vrho.reshape(1, -1)/2, 4*vgamma*rho[1:4]], axis=0) * grid_weights    # (4, grid_size)=(4, 45624)
+    grid_AO_T = grid_AO[0].T                                                                    # (N, gsize)
+    rho = jnp.expand_dims(rho, axis=2)                                                          # (4, gsize, 1)
+    grid_AO_rho = grid_AO * rho                                                                 # (4, gsize, N)
+    sum_grid_AO_rho = jnp.sum(grid_AO_rho, axis=0)                                              # (gsize, N)
+    V_xc = grid_AO_T @ sum_grid_AO_rho                                                          # (N, N)
+    V_xc = jax.lax.psum(V_xc, axis_name="p")                                                    # (N, N)
+    V_xc = V_xc + V_xc.T                                                                        # (N, N)
+    return E_xc, V_xc                                                                           # (float) (N, N)
 
 def get_JK(density_matrix, ERI, dense_ERI, backend):
     """Computes the (N, N) matrices J and K. Density matrix is (N, N) and ERI is (N, N, N, N).  """
@@ -201,15 +201,14 @@ def init_dft_tensors_cpu(mol, opts, DIIS_iters=9):
     grid_AO         = mol.eval_gto(coord_str, grids.coords, 4)      # (4, grid_size, N) = (4, 45624, 9) for C6H6.
     if opts.ao_threshold > 0.0:
         grid_AO[np.abs(grid_AO)<opts.ao_threshold] = 0
-        # trim grid_size (4, grid_size, N) by removing slices with all zeros along (0, 2) axes  
         sparsity_mask = np.where(np.all(grid_AO == 0, axis=0), 0, 1)
         sparse_rows = np.where(np.all(sparsity_mask == 0, axis=1), 0, 1).reshape(-1, 1)
-        grid_AO = jnp.delete(grid_AO, jnp.where(sparse_rows == 0)[0], axis=1)
-        grid_weights = jnp.delete(grid_weights, jnp.where(sparse_rows == 0)[0], axis=0)
-        grid_coords = jnp.delete(grids.coords, jnp.where(sparse_rows == 0)[0], axis=0)
         print(f"axis=( ,  ) sparsity in grid_AO: {np.sum(grid_AO==0) / grid_AO.size:.4f}")
         print(f"axis=(0,  ) sparsity in grid_AO: {np.sum(sparsity_mask==0) / sparsity_mask.size:.4f}")
         print(f"axis=(0, 2) sparsity in grid_AO: {np.sum(sparse_rows==0) / sparse_rows.size:.4f}")
+        grid_AO = jnp.delete(grid_AO, jnp.where(sparse_rows == 0)[0], axis=1)
+        grid_weights = jnp.delete(grid_weights, jnp.where(sparse_rows == 0)[0], axis=0)
+        grid_coords = jnp.delete(grids.coords, jnp.where(sparse_rows == 0)[0], axis=0)
     else:
         grid_coords = grids.coords
     density_matrix  = pyscf.scf.hf.init_guess_by_minao(mol)         # (N,N)=(66,66) for C6H6.