Sweep MVP

README.md

@@ -32,6 +32,12 @@ The following command will evaluate the probe from the run naughty-northcutt on
 elk eval naughty-northcutt microsoft/deberta-v2-xxlarge-mnli imdb
 ```
 
+The following runs `elicit` on the Cartesian product of the listed models and datasets, storing it in a special folder ELK_DIR/sweeps/<memorable_name>. Moreover, `--add_pooled` adds an additional dataset that pools all of the datasets together.
+
+```bash
+elk sweep --models gpt2-{medium,large,xl} --datasets imdb amazon_polarity --add_pooled
+```
+
 ## Caching
 
 The hidden states resulting from `elk elicit` are cached as a HuggingFace dataset to avoid having to recompute them every time we want to train a probe. The cache is stored in the same place as all other HuggingFace datasets, which is usually `~/.cache/huggingface/datasets`.

elk/__main__.py

@@ -5,21 +5,22 @@
 from simple_parsing import ArgumentParser
 
 from elk.evaluation.evaluate import Eval
+from elk.training.sweep import Sweep
 from elk.training.train import Elicit
 
 
 @dataclass
 class Command:
  """Some top-level command"""
 
- command: Elicit | Eval
+ command: Elicit | Eval | Sweep
 
  def execute(self):
  return self.command.execute()
 
 
 def run():
- parser = ArgumentParser(add_help=False, add_config_path_arg=True)
+ parser = ArgumentParser(add_help=False)
  parser.add_arguments(Command, dest="run")
  args = parser.parse_args()
  run: Command = args.run

elk/training/sweep.py

@@ -0,0 +1,60 @@
+from dataclasses import InitVar, dataclass
+
+from ..extraction import Extract, PromptConfig
+from ..files import elk_reporter_dir, memorably_named_dir
+from .train import Elicit
+
+
+@dataclass
+class Sweep:
+ models: list[str]
+ """List of Huggingface model strings to sweep over."""
+ datasets: list[str]
+ """List of dataset strings to sweep over. Each dataset string can contain
+ multiple datasets, separated by plus signs. For example, "sst2+imdb" will
+ pool SST-2 and IMDB together."""
+ add_pooled: InitVar[bool] = False
+ """Whether to add a dataset that pools all of the other datasets together."""
+
+ name: str | None = None
+
+ def __post_init__(self, add_pooled: bool):
+ if not self.datasets:
+ raise ValueError("No datasets specified")
+ if not self.models:
+ raise ValueError("No models specified")
+
+ # Add an additional dataset that pools all of the datasets together.
+ if add_pooled:
+ self.datasets.append("+".join(self.datasets))
+
+ def execute(self):
+ M, D = len(self.models), len(self.datasets)
+ print(f"Starting sweep over {M} models and {D} datasets ({M * D} runs)")
+ print(f"Models: {self.models}")
+ print(f"Datasets: {self.datasets}")
+
+ root_dir = elk_reporter_dir() / "sweeps"
+ sweep_dir = root_dir / self.name if self.name else memorably_named_dir(root_dir)
+ print(f"Saving sweep results to \033[1m{sweep_dir}\033[0m") # bold
+
+ for i, model_str in enumerate(self.models):
+ # Magenta color for the model name
+ print(f"\n\033[35m===== {model_str} ({i + 1} of {M}) =====\033[0m")
+
+ for dataset_str in self.datasets:
+ out_dir = sweep_dir / model_str / dataset_str
+
+ # Allow for multiple datasets to be specified in a single string with
+ # plus signs. This means we can pool datasets together inside of a
+ # single sweep.
+ datasets = [ds.strip() for ds in dataset_str.split("+")]
+ Elicit(
+ data=Extract(
+ model=model_str,
+ prompts=PromptConfig(
+ datasets=datasets,
+ ),
+ ),
+ out_dir=out_dir,
+ ).execute()