This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation, published at ICLR 2022.

Our dependencies are fully specified in Pipfile, which can be supplied to pipenv to install the environment. One failsafe approach is to install pipenv in a fresh virtual environment, then run pipenv install in this directory. Note the Pipfile specifies our Python 3.9 development environment; most experiments were run in an identical environment under Python 3.7 instead.

Difficulties with CUDA versions meant we had to manually install PyTorch and Torchvision rather than use pipenv --- the corresponding lines in Pipfile may need adjustment for your use case. Alternatively, use the list of dependencies as a guide to what to install yourself with pip, or use the full dump of our development environment in final_requirements.txt.

Datasets may not be bundled with the repository, but are expected to be found at locations specified in datasets.py, preprocessed into single PyTorch tensors of all the input and output data (generally data/<dataset>/data.pt and data/<dataset>/targets.pt). Running the script download_and_process_data.py will acquire and process all the necessary data into the required format.

Training code is controlled with YAML configuration files, as per the examples in configs/. Generally one file is required to specify the dataset, and a second to specify the algorithm, using the obvious naming convention. Brief help text is available on the command line, but the meanings of each option should be reasonably self-explanatory.

For Ours (WD+LR), use the file Ours_LR.yaml; for Ours (WD+LR+M), use the file Ours_LR_Momentum.yaml; for Ours (WD+HDLR+M), use the file Ours_HDLR_Momentum.yaml. For Long/Medium/Full Diff-through-Opt, we provide separate configuration files for the UCI cases and the Fashion-MNIST cases, labelled Standalone, Medium and Full respectively.

We provide two additional helper configurations. Random_Validation.yaml copies Random.yaml, but uses the entire validation set to compute the validation loss at each logging step. This allows for stricter analysis of the best-performing run at particular time steps, for instance while constructing Random (3-batched). Random_TrainingSetOnly.yaml also copies Random.yaml, but does not combine the training and validation datasets, so we can retain an unseen validation set for use by ASHA and PBT in the corresponding experiments. Random_Validation_BayesOpt.yaml only forces the use of the entire dataset for the very last validation loss computation, so that Bayesian Optimisation runs can access reliable performance metrics without adversely affecting runtime.

The configurations provided match those necessary to replicate the main experiments in our paper (in Section 4: Experiments). Other trials, such as those in the Appendix, will require these configurations to be modified as we describe in the paper.

The script run_all.py provides commands for running all experiments except those on PennTreebank and CIFAR-10, and will do so when run (but be warned, it will take weeks to finish!). Inspecting this file will reveal the configurations required for each individual experiment, which may facilitate use of the more granular execution commands described in the rest of this section.

Individual runs are commenced by executing train.py and passing the desired configuration files with the -c flag. For example, to run the default Fashion-MNIST experiments using Diff-through-Opt, use:

$ python train.py -c ./configs/fashion_mnist.yaml ./configs/DiffThroughOpt.yaml

Bayesian Optimisation runs are started in a similar way, but with a call to bayesopt.py rather than train.py.

For executing multiple runs in parallel, parallel_exec.py may be useful: modify the main function call at the bottom of the file as required, then call this file instead of train.py at the command line. The number of parallel workers may be specified by num_workers. Any configurations passed at the command line are used as a base, to which modifications may be added by override_generator. The latter should either be a function which generates one override dictionary per call (in which case num_repetitions sets the number of overrides to generate), or a function which returns a generator over configurations (in which case set num_repetitions = None). Each configuration override is run once for each of algorithms, whose configurations are read automatically from the corresponding files and should not be explicitly passed at the command line. Finally, main_function may be used to switch between parallel calls to train.py and bayesopt.py as required.

For blank-slate replications, the most useful override generators will be natural_sgd_generator, which generates a full SGD initialisation in the ranges we use, and iteration_id, which should be used with Bayesian Optimisation runs to name each parallel run using a counter. Other generators may be useful if you wish to supplement existing results with additional algorithms etc.

ASHA and PBT experiments are configured and executed by the functions ray_tune_run_asha and ray_tune_run_pbt in parallel_exec.py, which require the specification of a num_workers and num_repetitions as before, and additionally a name for the log folders. Note our implementation requires the __file__ property to contain an absolute path, which is only the case by default from Python 3.9. In earlier versions, this can be fixed with the snippet

__file__ = os.path.abspath("parallel_exec.py")

prior to calling either of the execution functions in parallel_exec.py.

PennTreebank and CIFAR-10 were executed on clusters running SLURM; the corresponding subfolders contain configuration scripts for these experiments, and submit.sh handles the actual job submission.

If you used run_all.py to execute the experiments, the function run_all.parse_results() may be useful to automate data extraction.

By default, runs are logged in Tensorboard format to the ./runs directory, where Tensorboard may be used to inspect the results. If desired, a descriptive name can be appended to a particular execution using the -n switch on the command line. Runs can optionally be written to a dedicated subfolder specified with the -g switch, and the base folder for logging can be changed with the -l switch.

If more precise analysis is desired, pass the directory containing the desired results to util.get_tags(), which will return a dictionary of the evolution of each logged scalar in the results. Note that this function uses Tensorboard calls which predate its --load_fast option, so may take tens of minutes to return.

This data dictionary can be passed to one of the more involved plotting routines in figures.py to produce specific plots. The script paper_plots.py generates all the plots we use in our paper, and may be inspected for details of any particular plot.