Merge branch 'main' of https://github.com/XanaduAI/DiffDFT

.github/workflows/install_and_test.yml

@@ -23,6 +23,9 @@ jobs:
       - name: Install extra example dependencies
         run: |
           pip install -e ".[examples]"
+      - name: Run unit tests
+        run: |
+            pytest -v tests/unit/test_eigenproblem.py
       - name: Run integration tests
         run: |
           pytest -v tests/integration/test_non_xc_energy.py

grad_dft/evaluate.py

@@ -32,9 +32,9 @@
 from grad_dft.utils import PyTree, Array, Scalar, Optimizer
 from grad_dft.functional import Functional
 
-from grad_dft.molecule import Molecule, abs_clip, make_rdm1, orbital_grad, general_eigh
+from grad_dft.molecule import Molecule, abs_clip, make_rdm1, orbital_grad
 from grad_dft.train import molecule_predictor
-from grad_dft.utils import PyTree, Array, Scalar
+from grad_dft.utils import PyTree, Array, Scalar, safe_fock_solver
 from grad_dft.interface.pyscf import (
     generate_chi_tensor,
     mol_from_Molecule,
@@ -117,7 +117,7 @@ def scf_iterator(params: PyTree, molecule: Molecule, *args) -> Tuple[Scalar, Sca
             else:
                 # Diagonalize Fock matrix
                 overlap = abs_clip(molecule.s1e, 1e-20)
-                mo_energy, mo_coeff = general_eigh(fock, overlap)
+                mo_energy, mo_coeff = safe_fock_solver(fock, overlap)
                 molecule = molecule.replace(mo_coeff=mo_coeff)
                 molecule = molecule.replace(mo_energy=mo_energy)
 
@@ -275,7 +275,7 @@ def scf_iterator(params: PyTree, molecule: Molecule, *args) -> Tuple[Scalar, Sca
                 )
 
             # Diagonalize Fock matrix
-            mo_energy, mo_coeff = general_eigh(fock, molecule.s1e)
+            mo_energy, mo_coeff = safe_fock_solver(fock, molecule.s1e)
             molecule = molecule.replace(mo_coeff=mo_coeff)
             molecule = molecule.replace(mo_energy=mo_energy)
 
@@ -373,7 +373,7 @@ def nelec_cost_fn(m, mo_es, sigma, _nelectron):
         if abs(predicted_e - old_e) * Hartree2kcalmol < e_conv and norm_gorb < g_conv:
             # We perform an extra diagonalization to remove the level shift
             # Solve eigenvalue problem
-            mo_energy, mo_coeff = general_eigh(fock, molecule.s1e)
+            mo_energy, mo_coeff = safe_fock_solver(fock, molecule.s1e)
             molecule = molecule.replace(mo_coeff=mo_coeff)
             molecule = molecule.replace(mo_energy=mo_energy)
 
@@ -663,7 +663,7 @@ def loop_body(cycle, state):
             fock, diis_data = diis.run(new_data, diis_data, cycle)
 
             # Diagonalize Fock matrix
-            mo_energy, mo_coeff = general_eigh(fock, molecule.s1e)
+            mo_energy, mo_coeff = safe_fock_solver(fock, molecule.s1e)
             molecule = molecule.replace(mo_coeff=mo_coeff)
             molecule = molecule.replace(mo_energy=mo_energy)
 

grad_dft/external/__init__.py

@@ -8,4 +8,3 @@
 from grad_dft.external.density_functional_approximation_dm21.density_functional_approximation_dm21.neural_numint import (
     _SystemState,
 )
-from grad_dft.external.eigh_impl import eigh2d

grad_dft/molecule.py

@@ -16,7 +16,6 @@
 from dataclasses import fields
 from grad_dft.utils import vmap_chunked
 from functools import partial
-from grad_dft.external.eigh_impl import eigh2d
 
 from jax import numpy as jnp
 from jax import scipy as jsp
@@ -615,27 +614,9 @@ def chunked_jvp(chi_tensor, gr_tensor, ao_tensor):
 
     return (jax.jit(chunked_jvp)(chi.transpose(3, 0, 1, 2), gr, ao)).transpose(1, 2, 3, 0)
 
-
-def eig(h, x):
-    e0, c0 = eigh2d(h[0], x)
-    e1, c1 = eigh2d(h[1], x)
-    return jnp.stack((e0, e1), axis=0), jnp.stack((c0, c1), axis=0)
-
 def abs_clip(arr, threshold):
     return jnp.where(jnp.abs(arr) > threshold, arr, 0)
 
-def general_eigh(A, B):
-    L = jnp.linalg.cholesky(B)
-    L_inv = jnp.linalg.inv(L)
-    C = L_inv @ A @ L_inv.T
-    C = abs_clip(C, 1e-20)
-    eigenvalues, eigenvectors_transformed = jnp.linalg.eigh(C)
-    # eigenvalues, eigenvectors_transformed = jsp.linalg.eigh(C)
-    eigenvectors_original = L_inv.T @ eigenvectors_transformed
-    eigenvectors_original = abs_clip(eigenvectors_original, 1e-20)
-    eigenvalues = abs_clip(eigenvalues, 1e-20)
-    return eigenvalues, eigenvectors_original
-
 
 ######################################################################
 

grad_dft/utils/__init__.py

@@ -28,3 +28,4 @@
 from .tree import tree_size, tree_isfinite, tree_randn_like, tree_func, tree_shape
 from .utils import to_device_arrays, Utils
 from .chunk import vmap_chunked
+from .eigenproblem import safe_fock_solver

grad_dft/utils/eigenproblem.py

@@ -0,0 +1,144 @@
+# Copyright 2023 Xanadu Quantum Technologies Inc.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import jax
+import jax.numpy as jnp
+from jax import custom_vjp
+from .types import Array, Scalar
+
+# Probably don't alter these unless you know what you're doing
+DEGEN_TOL = 1e-6
+BROADENING = 1e-7
+
+
+@custom_vjp
+def safe_eigh(A: Array) -> tuple[Array, Array]:
+    r"""Get the eigenvalues and eigenvectors for an input real symmetric matrix.
+        A safe reverse mode gradient is implemented in safe_eigh_rev below.
+
+    Args:
+        A (Array): a 2D Jax array representing a real symmetric matrix.
+
+    Returns:
+        tuple[Array, Array]: the eigenvalues and eigenvectors of the input real symmetric matrix.
+    """
+    evecs, evals = jnp.linalg.eigh(A)
+    return evecs, evals
+
+
+def safe_eigh_fwd(A: Array) -> tuple[tuple[Array, Array], tuple[tuple[Array, Array], Array]]:
+    r"""Forward mode operation of safe_eigh. Saves evecs and evals for the reverse pass.
+
+    Args:
+        A (Array): a 2D Jax array representing a real symmetric matrix.
+
+    Returns:
+        tuple[tuple[Array, Array], tuple[tuple[Array, Array], Array]]: eigenvectors, eigenvalues and the input real symmetric matrix A.
+    """
+    evecs, evals = safe_eigh(A)
+    return (evecs, evals), ((evecs, evals), A)
+
+
+def safe_eigh_rev(res: tuple[tuple[Array, Array], Array], g: Array) -> tuple[Array]:
+    r"""Use the Lorentzian broading approach suggested in https://doi.org/10.1038/s42005-021-00568-6
+        to calculate stable backward mode gradients for degenerate eigenvectors. We only apply this
+        technique if eigenvalues are detected to be degenerate according to the constant DEGEN_TOL
+        in this module. When degeneracies are detected, the are broadened according to the constant
+        BROADENING also defined in this module.
+
+    Args:
+        res (tuple[tuple[Array, Array]): eigenvectors, eigenvales and the input real symmetric matrix A saved from the forward pass
+        g (Array): the gradients d[eigenvalues]/dA and d[eigenvectors]/dA
+
+    Returns:
+        tuple[Array]: the matrix of reverse mode gradients.
+
+    """
+    (evals, evecs), A = res
+    grad_evals, grad_evecs = g
+    grad_evals_diag = jnp.diag(grad_evals)
+    evecs_trans = evecs.T
+
+    # Generate eigenvalue difference matrix
+    eval_diff = evals.reshape((1, -1)) - evals.reshape((-1, 1))
+    # Find elements where degen_tol condition was or wasn't was met
+    mask_degen = (jnp.abs(eval_diff) < DEGEN_TOL).astype(jnp.int32)
+    mask_non_degen = (jnp.abs(eval_diff) >= DEGEN_TOL).astype(jnp.int32)
+
+    # Regular gap for non_degen terms => 1/(e_j - e_i)
+    # Will get +infs turning to large numbers here if degeneracies are present.
+    # This doesn't matter as they multiply by 0 in the forthcoming mask when calculating
+    # the F-matrix
+    regular_gap = jnp.nan_to_num(jnp.divide(1, eval_diff))
+
+    # Lorentzian broadened gap for degen terms => (e_j - e_i)/((e_j - e_i)^2 + eps)
+    broadened_gap = eval_diff / (eval_diff * eval_diff + BROADENING)
+
+    # Calculate full F matrix. large numbers generated by NaNs from regular_gap are deleted here
+    F = 0.5 * (jnp.multiply(mask_non_degen, regular_gap) + jnp.multiply(mask_degen, broadened_gap))
+
+    # Set diagonals to 0
+    F = F.at[jnp.diag_indices_from(F)].set(0)
+
+    # Calculate the gradient
+    grad = (
+        jnp.linalg.inv(evecs_trans)
+        @ (0.5 * grad_evals_diag + jnp.multiply(F, evecs_trans @ grad_evecs))
+        @ evecs_trans
+    )
+    # Symmetrize
+    grad_sym = grad + grad.T
+    return (grad_sym,)
+
+
+safe_eigh.defvjp(safe_eigh_fwd, safe_eigh_rev)
+
+
+def safe_general_eigh(A: Array, B: Array) -> tuple[Array, Array]:
+    r"""Solve the general eigenproblem for the eigenvalues and eigenvectors. I.e,
+        . math::
+            AC = ECB
+        for matrix of eigenvectors C and diagonal matrix of eigenvalues E. This function requires all input
+        matrices to real and symmetric and the matrix B to be invertible.
+
+    Args:
+        A (Array): a real symmetric matrix
+        B (Array): another real symmetric matrix
+
+    Returns:
+        tuple[Array, Array]: the eigenvalues and matrix of eigenvectors
+    """
+    L = jnp.linalg.cholesky(B)
+    L_inv = jnp.linalg.inv(L)
+    C = L_inv @ A @ L_inv.T
+    eigenvalues, eigenvectors_transformed = safe_eigh(C)
+    eigenvectors_original = L_inv.T @ eigenvectors_transformed
+    return eigenvalues, eigenvectors_original
+
+
+def safe_fock_solver(fock: tuple[Array, Array], overlap: Array) -> tuple[Array, Array]:
+    """Get the eigenenergies and molecular orbital coefficients for the
+        up and down fock spin matrices.
+    Args:
+        fock (tuple[Array, Array]): the up and down fock spin matrices
+        overlap (Array): the overlap matrix
+
+    Returns:
+        tuple[Array, Array]: the eigenenergies and matrix of molecular orbital coefficients.
+    """
+    mo_energies_up, mo_coeffs_up = safe_general_eigh(fock[0], overlap)
+    mo_energies_dn, mo_coeffs_dn = safe_general_eigh(fock[1], overlap)
+    return jnp.stack((mo_energies_up, mo_energies_dn), axis=0), jnp.stack(
+        (mo_coeffs_up, mo_coeffs_dn), axis=0
+    )