Tutorial

This tutorial will explain how to use the HELM command line tools to run benchmarks, aggregate statistics, and visualize results.

We will run two runs using the mmlu scenario on the openai/gpt2 model. The mmlu scenario implements the Massive Multitask Language (MMLU) benchmark from this paper, and consists of a Question Answering (QA) task using a dataset with questions from 57 subjects such as elementary mathematics, US history, computer science, law, and more. Note that GPT-2 performs poorly on MMLU, so this is just a proof of concept. We will run two runs: the first using questions about anatomy, and the second using questions about philosophy.

Using helm-run

helm-run is a command line tool for running benchmarks.

To run this benchmark using the HELM command-line tools, we need to specify run entries that describes the desired runs. For this example, the run entries are mmlu:subject=anatomy,model=openai/gpt2 (for anatomy) and mmlu:subject=philosophy,model=openai/gpt2 (for philosophy).

We will now use helm-run to execute the runs. Run this command:

helm-run --run-entries mmlu:subject=anatomy,model=openai/gpt2 mmlu:subject=philosophy,model=openai/gpt2 --suite my-suite --max-eval-instances 10

The meaning of the arguments are as follows:

  • --run-entries specifies the run entries from the desired runs.
  • --suite specifies a subdirectory under the output directory in which all the output will be placed.
  • --max-eval-instances limits evaluation to only N instances (i.e. items) from the benchmark, using a randomly shuffled order of instances.

helm-run creates an environment directory environment and an output directory by default.

  • The environment directory is prod_env/ by default and can be set using --local-path. Credentials for making API calls should be added to a credentials.conf file in this directory.
  • The output directory is benchmark_output/ by default and can be set using --output-path.

After running this command, navigate to the benchmark_output/runs/my-suite/ directory. This should contain a two sub-directories named mmlu:subject=anatomy,model=openai_gpt2 and mmlu:subject=philosophy,model=openai_gpt2. Note that the names of these sub-directories is based on the run entries we used earlier, but with / replaced with _.

Each output sub-directory will contain several JSON files that were generated during the corresponding run:

  • run_spec.json contains the RunSpec, which specifies the scenario, adapter and metrics for the run.
  • scenario.json contains a serialized Scenario, which contains the scenario for the run and specifies the instances (i.e. inputs) used.
  • scenario_state.json contains a serialized ScenarioState, which contains every request to and response from the model.
  • per_instance_stats.json contains a serialized list of PerInstanceStats, which contains the statistics produced for the metrics for each instance (i.e. input).
  • stats.json contains a serialized list of PerInstanceStats, which contains the statistics produced for the metrics, aggregated across all instances (i.e. inputs).

Using helm-summarize

The helm-summarize reads the output files of helm-run and computes aggregate statistics across runs. Run the following:

helm-summarize --suite my-suite

This reads the pre-existing files in benchmark_output/runs/my-suite/ that were written by helm-run previously, and writes the following new files back to benchmark_output/runs/my-suite/:

  • summary.json contains a serialized ExecutiveSummary with a date and suite name.
  • run_specs.json contains the run entries for all the runs.
  • runs.json contains serialized list of Run, which contains the run path, run spec and adapter spec and statistics for each run.
  • groups.json contains a serialized list of Table, each containing information about groups in a group category.
  • groups_metadata.json contains a list of all the groups along with a human-readable description and a taxonomy.

Additionally, for each group and group-relavent metric, it will output a pair of files: benchmark_output/runs/my-suite/groups/latex/<group_name>_<metric_name>.tex and benchmark_output/runs/my-suite/groups/json/<group_name>_<metric_name>.json. These files contain the statistics for that metric from each run within the group.

Using helm-server

Finally, the helm-server command launches a web server to visualize the output files of helm-run and helm-benchmark. Run:

helm-server --suite my-suite

Open a browser and go to http://localhost:8000/ to view the visualization. You should see a similar view as live website for the paper, but for the data from your benchmark runs. The website has the following sections accessible from the top menu bar:

  • Leaderboards contains the leaderboards with aggregate metrics.
  • Models contains a list of models and their descriptions
  • Scenarios contains a list of scenarios and their descriptions.
  • Predictions contains a searchable list of runs.