Add NNCG to optimizers submodule (#1661)

deepxde/model.py

@@ -367,11 +367,22 @@ def closure():
             if self.lr_scheduler is not None:
                 self.lr_scheduler.step()
 
+        def train_step_nncg(inputs, targets, auxiliary_vars):
+            def closure():
+                losses = outputs_losses_train(inputs, targets, auxiliary_vars)[1]
+                total_loss = torch.sum(losses)
+                self.opt.zero_grad()
+                return total_loss
+
+            self.opt.step(closure)
+            if self.lr_scheduler is not None:
+                self.lr_scheduler.step()
+
         # Callables
         self.outputs = outputs
         self.outputs_losses_train = outputs_losses_train
         self.outputs_losses_test = outputs_losses_test
-        self.train_step = train_step
+        self.train_step = train_step if self.opt_name != "NNCG" else train_step_nncg
 
     def _compile_jax(self, lr, loss_fn, decay):
         """jax"""
@@ -652,7 +663,10 @@ def train(
             elif backend_name == "tensorflow":
                 self._train_tensorflow_tfp(verbose=verbose)
             elif backend_name == "pytorch":
-                self._train_pytorch_lbfgs(verbose=verbose)
+                if self.opt_name == "L-BFGS":
+                    self._train_pytorch_lbfgs(verbose=verbose)
+                elif self.opt_name == "NNCG":
+                    self._train_sgd(iterations, display_every, verbose=verbose)
             elif backend_name == "paddle":
                 self._train_paddle_lbfgs(verbose=verbose)
         else:

deepxde/optimizers/__init__.py

@@ -1,7 +1,7 @@
 import importlib
 import sys
 
-from .config import LBFGS_options, set_LBFGS_options
+from .config import LBFGS_options, set_LBFGS_options, NNCG_options, set_NNCG_options
 from ..backend import backend_name
 
 

deepxde/optimizers/config.py

@@ -1,9 +1,10 @@
-__all__ = ["set_LBFGS_options", "set_hvd_opt_options"]
+__all__ = ["set_LBFGS_options", "set_NNCG_options", "set_hvd_opt_options"]
 
 from ..backend import backend_name
 from ..config import hvd
 
 LBFGS_options = {}
+NNCG_options = {}
 if hvd is not None:
     hvd_opt_options = {}
 
@@ -60,6 +61,60 @@ def set_LBFGS_options(
     LBFGS_options["maxls"] = maxls
 
 
+def set_NNCG_options(
+    lr=1,
+    rank=50,
+    mu=1e-1,
+    updatefreq=20,
+    chunksz=1,
+    cgtol=1e-16,
+    cgmaxiter=1000,
+    lsfun="armijo",
+    verbose=False,
+):
+    """Sets the hyperparameters of NysNewtonCG (NNCG).
+
+    The NNCG optimizer only supports PyTorch.
+
+    Args:
+        lr (float):
+            Learning rate (before line search).
+        rank (int):
+            Rank of preconditioner matrix used in preconditioned conjugate gradient.
+        mu (float):
+            Hessian damping parameter.
+        updatefreq (int):
+            How often the preconditioner matrix in preconditioned
+            conjugate gradient is updated. This parameter is not directly used in NNCG,
+            instead it is used in _train_pytorch_nncg in deepxde/model.py.
+        chunksz (int):
+            Number of Hessian-vector products to compute in parallel when constructing
+            preconditioner. If `chunk_size` is 1, the Hessian-vector products are
+            computed serially.
+        cgtol (float):
+            Convergence tolerance for the conjugate gradient method. The iteration stops
+            when `||r||_2 <= cgtol`, where `r` is the residual. Note that this condition
+            is based on the absolute tolerance, not the relative tolerance.
+        cgmaxiter (int):
+            Maximum number of iterations for the conjugate gradient method.
+        lsfun (str):
+            The line search function used to find the step size. The default value is
+            "armijo". The other option is None.
+        verbose (bool):
+            If `True`, prints the eigenvalues of the Nyström approximation
+            of the Hessian.
+    """
+    NNCG_options["lr"] = lr
+    NNCG_options["rank"] = rank
+    NNCG_options["mu"] = mu
+    NNCG_options["updatefreq"] = updatefreq
+    NNCG_options["chunksz"] = chunksz
+    NNCG_options["cgtol"] = cgtol
+    NNCG_options["cgmaxiter"] = cgmaxiter
+    NNCG_options["lsfun"] = lsfun
+    NNCG_options["verbose"] = verbose
+
+
 def set_hvd_opt_options(
     compression=None,
     op=None,
@@ -91,6 +146,7 @@ def set_hvd_opt_options(
 
 
 set_LBFGS_options()
+set_NNCG_options()
 if hvd is not None:
     set_hvd_opt_options()