PyTorchHessianFree

Here, we provide a PyTorch implementation of the Hessian-free optimizer as described in [1] and [2] (see below). This project is currently still being developed, so changes may be made at any time.

Core idea: At each step, the optimizer computes a local quadratic approximation of the target function and uses the conjugate gradient (cg) method to approximate its minimum (the Newton step). This method only requires access to matrix-vector products with the curvature matrix, which can be done without creating this matrix in memory explicitly. This makes the Hessian-free optimizer applicable for large problems with high-dimensional parameter spaces (e.g. training neural networks).

Credits: The pytorch-hessianfree repo by GitHub-user fmeirinhos served as a starting point. For the matrix-vector products with the Hessian or GGN, we use functionality from the BackPACK package [3].

Table of contents:

1. Installation instructions
2. Example
3. Structure of this repo
4. Implementation details
5. Contributing
6. References

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1. Installation instructions

If you want to use the optimizer, you can download the repo from GitHub via git clone https://github.com/ltatzel/PyTorchHessianFree.git. Then, navigate to the project folder cd PyTorchHessianFree and install it with pip install -e .. Or install the package directly from GitHub via pip install hessianfree git+https://git@github.com/ltatzel/PyTorchHessianFree.git@main.

Additional requirements for the tests and examples can be installed via pip install -e ".[tests]" and pip install -e ".[examples]" respectively. For running the tests, execute pytest from the repo's root directory.

2. Example

"""A minimal working example using the `HessianFree` optimizer on a small neural
network and some dummy data.
"""

import torch

from hessianfree.optimizer import HessianFree

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
BATCH_SIZE = 16
DIM = 10

if __name__ == "__main__":

    # Set up model, loss-function and optimizer
    model = torch.nn.Sequential(
        torch.nn.Linear(DIM, DIM, bias=False),
        torch.nn.ReLU(),
        torch.nn.Linear(DIM, DIM),
    ).to(DEVICE)
    loss_function = torch.nn.MSELoss()
    opt = HessianFree(model.parameters(), verbose=True)

    # Training
    for step_idx in range(5):

        # Sample dummy data, define the `forward`-function
        inputs = torch.rand(BATCH_SIZE, DIM).to(DEVICE)
        targets = torch.rand(BATCH_SIZE, DIM).to(DEVICE)

        def forward():
            outputs = model(inputs)
            loss = loss_function(outputs, targets)
            return loss, outputs

        # Update the model's parameters
        opt.step(forward=forward)

3. Structure of this repo

The repo contains three folders:

• hessianfree: This folder contains all the optimizer's components (e.g. the line search, the cg-method and preconditioners). The Hessian-free optimizer itself is implemented in the optimizer.py file.
• tests: Here, we test functionality implemented in hessianfree.
• examples: This folder contains a few basic examples demonstrating how to use the optimizer for training neural networks (using the step and acc_step method) and optimizing deterministic functions (e.g. the Rosenbrock function).

4. Implementation details

• Hessian & GGN: Our implementation allows using either the Hessian matrix or the GGN as curvature matrix via the argument curvature_opt to the optimizer's constructor. As recommended in [1, Section 4.2] and [2, e.g. p. 10], the default is the symmetric positive semidefinite GGN. For the matrix-vector products with these matrices, we use functionality from the BackPACK package [3].

• Damping: As described in [1, Section 4.1], Tikhonov-damping can be used to avoid overly large steps. Our implementation also features the Levenberg-Marquardt style heuristic for adjusting the damping parameter - it can be turned on and off via the adapt_damping switch.

• PCG: Our implementation of the preconditioned conjugate gradient method features the termination criterion presented in [1, Section 4.4] via the argument martens_conv_crit.

  As suggested in [1, Section 4.5], we use the cg-"solution" from the last step as a starting point for the next one. Via the argument cg_decay_x0 to the optimizer's constructor, this initial search direction can be scaled by a constant. The default is 0.95 as in [2, Section 10].

  The get_preconditioner-method implements the preconditioner suggested in [1, Section 4.7]: The diagonal of the empirical Fisher matrix.

• CG-backtracking & line search: When cg-backtracking is used, the cg-method will return not only the final "solution" to the linear system but also intermediate "solutions" for a subset of the iterations. This grid of iterations is generated using the approach from [1, Section 4.6]. In a subsequent step, the set of potential update steps is searched for an "ideal" candidate.

  Next, this update step is iteratively scaled back by the line search until the target function is decreased "significantly" (Armijo condition). This approach is described in [2, Section 8.8].

  Both these modules are optional and can be turned on and off via the switches use_cg_backtracking and use_linesearch.

• Computing parameter updates: Our Hessian-free optimizer offers two methods for computing parameter updates: step and acc_step.

  The former one, which is also used in the example above, only has one required argument: the forward-function. This represents the target function and all relevant quantities needed by the optimizer (e.g. the gradient and curvature information) are deduced from this function.

  You may want to use the latter method acc_step if you run out of memory when training your neural network model using step or if you want to evaluate the target function value (the loss), gradient and curvature on different data sets (this is actually recommended since it reduces mini-batch overfitting). The acc_step method allows you to specify (potentially different) lists of data for these three quantities. It evaluates e.g. the gradient only on one list entry (i.e. one mini-batch) at a time and accumulates the individual gradients automatically. This iterative approach slows down the computations but enables us to work with very large data sets. A basic example can be found here.

5. Contributing

I would be very grateful for any feedback! If you have questions, a feature request, found a bug or have comments on how to improve the code, please don't hesitate to reach out to me.

6. References

[1] "Deep learning via Hessian-free optimization" by James Martens. In Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML), 2010. Paper available at https://www.cs.toronto.edu/~jmartens/docs/Deep_HessianFree.pdf (accessed June 2022).

[2] "Training Deep and Recurrent Networks with Hessian-Free Optimization" by James Martens and Ilya Sutskever. Report available at https://www.cs.utoronto.ca/~jmartens/docs/HF_book_chapter.pdf (accessed June 2022).

[3] "BackPACK: Packing more into Backprop" by Felix Dangel, Frederik Kunstner and Philipp Hennig. In International Conference on Learning Representations, 2020. Paper available at https://openreview.net/forum?id=BJlrF24twB (accessed June 2022). Python package available at https://github.com/f-dangel/backpack.