Add to_closure utility

descent/optim/_lm.py

@@ -20,7 +20,8 @@
 
 
 ClosureFn = typing.Callable[
-    [torch.Tensor, bool, bool], tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+    [torch.Tensor, bool, bool],
+    tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None],
 ]
 CorrectFn = typing.Callable[[torch.Tensor], torch.Tensor]
 
@@ -100,19 +101,19 @@ class LevenbergMarquardtConfig(pydantic.BaseModel):
     )
 
     convergence_loss: float = pydantic.Field(
-        1.0e-4,
+        0.1,
         description="The loss will be considered converged if its std deviation over "
         "the last `n_convergence_steps` steps is less than this value.",
         gt=0.0,
     )
     convergence_gradient: float = pydantic.Field(
-        1.0e-3,
+        0.1,
         description="The gradient will be considered converged if its norm is less than"
         "this value.",
         gt=0.0,
     )
     convergence_step: float = pydantic.Field(
-        1.0e-4,
+        0.01,
         description="The step size will be considered converged if its norm is less "
         "than this value.",
         gt=0.0,
@@ -123,7 +124,7 @@ class LevenbergMarquardtConfig(pydantic.BaseModel):
         "in the loss.",
     )
     n_convergence_criteria: int = pydantic.Field(
-        1,
+        2,
         description="The number of convergence criteria that must be satisfied before "
         "the optimization is considered converged. If 0, no convergence criteria will "
         "be used and the optimizer will run for ``max_steps`` full steps.",

descent/tests/utils/test_loss.py

@@ -0,0 +1,37 @@
+import torch
+
+from descent.utils.loss import to_closure
+
+
+def test_to_closure():
+    def mock_loss_fn(x: torch.Tensor, a: float, b: float) -> torch.Tensor:
+        return (a * x**2 + b).sum()
+
+    closure_fn = to_closure(mock_loss_fn, a=2.0, b=3.0)
+
+    theta = torch.Tensor([1.0, 2.0]).requires_grad_(True)
+
+    expected_loss = torch.tensor(16.0)
+    expected_grad = torch.tensor([4.0, 8.0])
+    expected_hess = torch.tensor([[4.0, 0.0], [0.0, 4.0]])
+
+    loss, grad, hess = closure_fn(theta, True, True)
+
+    assert loss.shape == expected_loss.shape
+    assert torch.allclose(loss, expected_loss)
+
+    assert grad.shape == expected_grad.shape
+    assert torch.allclose(grad, expected_grad)
+
+    assert hess.shape == expected_hess.shape
+    assert torch.allclose(hess, expected_hess)
+
+    loss, grad, hess = closure_fn(theta, False, True)
+    assert loss is not None
+    assert grad is None
+    assert hess is not None
+
+    loss, grad, hess = closure_fn(theta, True, False)
+    assert loss is not None
+    assert grad is not None
+    assert hess is None

descent/utils/loss.py

@@ -0,0 +1,53 @@
+"""Utilities for defining loss functions."""
+import functools
+import typing
+
+import torch
+
+ClosureFn = typing.Callable[
+    [torch.Tensor, bool, bool],
+    tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None],
+]
+P = typing.ParamSpec("P")
+
+
+def to_closure(
+    loss_fn: typing.Callable[typing.Concatenate[torch.Tensor, P], torch.Tensor],
+    *args: P.args,
+    **kwargs: P.kwargs,
+) -> ClosureFn:
+    """Convert a loss function to a closure function used by second-order optimizers.
+
+    Args:
+        loss_fn: The loss function to convert. This should take in a tensor of
+            parameters with ``shape=(n,)``, and optionally a set of ``args`` and
+            ``kwargs``.
+        *args: Positional arguments passed to `loss_fn`.
+        **kwargs: Keyword arguments passed to `loss_fn`.
+
+    Returns:
+        A closure function that takes in a tensor of parameters with ``shape=(n,)``,
+        a boolean flag indicating whether to compute the gradient, and a boolean flag
+        indicating whether to compute the Hessian. It returns a tuple of the loss
+        value, the gradient, and the Hessian.
+    """
+
+    loss_fn_wrapped = functools.partial(loss_fn, *args, **kwargs)
+
+    def closure_fn(
+        x: torch.Tensor, compute_gradient: bool, compute_hessian: bool
+    ) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]:
+        loss = loss_fn_wrapped(x)
+        gradient, hessian = None, None
+
+        if compute_hessian:
+            hessian = torch.autograd.functional.hessian(
+                loss_fn_wrapped, x, vectorize=True, create_graph=False
+            ).detach()
+        if compute_gradient:
+            (gradient,) = torch.autograd.grad(loss, x, create_graph=False)
+            gradient = gradient.detach()
+
+        return loss.detach(), gradient, hessian
+
+    return closure_fn