Computing directionally damped Newton steps

In this example we demonstrate how to use ViViT’s DirectionalDampedNewtonComputation to compute directionally damped Newton steps with the GGN. We verify the result with torch.autograd.

First, the imports.

from typing import List

from backpack import backpack, extend
from backpack.utils.examples import _autograd_ggn_exact_columns
from torch import (
from torch.autograd import grad
from torch.nn import Linear, MSELoss, ReLU, Sequential
from torch.nn.utils.convert_parameters import parameters_to_vector

from vivit.optim.directional_damped_newton import DirectionalDampedNewtonComputation

# make deterministic


<torch._C.Generator object at 0x7fc15a5db370>

Data, model & loss

For this demo, we use toy data and a small MLP with sufficiently few parameters such that we can store the GGN matrix to verify our results. We use mean squared error as loss function.

N = 4
D_in = 7
D_hidden = 5
D_out = 3

DEVICE = device("cuda" if cuda.is_available() else "cpu")

X = rand(N, D_in).to(DEVICE)
y = rand(N, D_out).to(DEVICE)

model = Sequential(
    Linear(D_in, D_hidden),
    Linear(D_hidden, D_hidden),
    Linear(D_hidden, D_out),

loss_function = MSELoss(reduction="mean").to(DEVICE)

Integrate BackPACK

Next, extend the model and loss function to be able to use BackPACK. Then, we perform a forward pass to compute the loss.

Specify GGN approximation and directions

By default, vivit.DirectionalDampedNewtonComputation uses the exact GGN. Furthermore, we need to specify the GGN’s parameters via a param_groups argument that might be familiar to you from torch.optim. It also contains a filter function that selects the eigenvalues whose eigenvectors will be used as directions for the Newton step.

computation = DirectionalDampedNewtonComputation()

def select_top_k(evals: Tensor, k=4) -> List[int]:
    """Select the top-k eigenvalues as directions to evaluate derivatives.

        evals: Eigenvalues, sorted in ascending order.
        k: Number of leading eigenvalues. Defaults to ``4``.

        Indices of top-k eigenvalues.
    return [evals.numel() - k + idx for idx in range(k)]

Specify directional damping

We also need a damping function that provides the damping value for each direction. This function receives the GGNs eigenvalues, Gram matrix eigenvectors, as well as first- and second-order directional derivatives. It returns a one-dimensional tensor that contains the damping values for all directions.

This seems overly complicated. But this approach allows for incorporating information about gradient and curvature noise into the damping value.

For simplicity, we will use a constant damping of 1 for all directions.


def constant_damping(
    evals: Tensor, evecs: Tensor, gammas: Tensor, lambdas: Tensor
) -> Tensor:
    """Constant damping along all directions.

        evals: GGN eigenvalues. Shape ``[K]``.
        evecs: GGN Gram matrix eigenvectors. Shape ``[NC, K]``.
        gammas: Directional gradients. Shape ``[N, K]``.
        lambdas: Directional curvatures. Shape ``[N, K]``.

        Directional dampings. Shape ``[K]``.
    return DAMPING * ones_like(evals)

Let’s put everything together and set up the parameter groups.

group = {
    "params": [p for p in model.parameters() if p.requires_grad],
    "criterion": select_top_k,
    "damping": constant_damping,
param_groups = [group]

Backward pass with BackPACK

We can now build the BackPACK extensions and extension hook that will compute the damped Newton step, pass them to a with backpack, and perform the backward pass.

extensions = computation.get_extensions()
extension_hook = computation.get_extension_hook(param_groups)

with backpack(*extensions, extension_hook=extension_hook):

This will compute the damped Newton step for each parameter group and store it internally in the vivit.DirectionalDampedNewtonComputation instance. We can use the parameter group to request it.

newton_step = computation.get_result(group)

It has the same format as the group['params'] entry:

for param, newton in zip(group["params"], newton_step):
    print(f"Parameter shape:   {param.shape}\nNewton step shape: {newton.shape}\n")


Parameter shape:   torch.Size([5, 7])
Newton step shape: torch.Size([5, 7])

Parameter shape:   torch.Size([5])
Newton step shape: torch.Size([5])

Parameter shape:   torch.Size([5, 5])
Newton step shape: torch.Size([5, 5])

Parameter shape:   torch.Size([5])
Newton step shape: torch.Size([5])

Parameter shape:   torch.Size([3, 5])
Newton step shape: torch.Size([3, 5])

Parameter shape:   torch.Size([3])
Newton step shape: torch.Size([3])

We will flatten and concatenate the Newton step over parameters to simplify the comparison with torch.autograd.



Verify results

Let’s compute the damped Newton step with torch.autograd and verify it leads to the same result.

We need the gradient and the GGN.

gradient = grad(
    loss_function(model(X), y), [p for p in model.parameters() if p.requires_grad]
gradient = parameters_to_vector(gradient)

ggn = stack([col for _, col in _autograd_ggn_exact_columns(X, y, model, loss_function)])

print(gradient.shape, ggn.shape)


torch.Size([88]) torch.Size([88, 88])

Next, eigen-decompose the GGN and filter the relevant eigenpairs:

evals, evecs = ggn.symeig(eigenvectors=True)
keep = select_top_k(evals)
evals, evecs = evals[keep], evecs[:, keep]

This is sufficient to form the damped Newton step

\[s = \sum_{k=1}^K \frac{-\gamma_k}{\lambda_k + \delta} e_k\]

with constant damping \(\delta = 1\).

newton_step_torch = zeros_like(gradient)

K = evals.numel()

for k in range(K):
    evec = evecs[:, k]
    gamm = einsum("i,i", gradient, evec)
    lamb = evals[k]

    newton = (-gamm / (lamb + DAMPING)) * evec
    newton_step_torch += newton




Both damped Newton steps should be identical.

close = allclose(newton_step_flat, newton_step_torch, rtol=1e-5, atol=1e-7)
if not close:
    raise ValueError("Directionally damped Newton steps don't match!")

print("Directionally damped Newton steps match!")


Directionally damped Newton steps match!

Total running time of the script: ( 0 minutes 0.129 seconds)

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