Add self-prompting eval

evals/elsuite/self_prompting/eval.py

@@ -0,0 +1,261 @@
+import json
+import logging
+import random
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import numpy as np
+
+import evals
+import evals.metrics
+from evals.api import CompletionFn
+from evals.elsuite.self_prompting.task_description import sample_in_token, task_description_template
+from evals.eval import SolverEval
+from evals.registry import registry
+from evals.solvers.solver import Solver
+from evals.task_state import TaskState
+from evals.utils.log_utils import extract_final_results, extract_spec
+
+logger = logging.getLogger(__name__)
+
+
+class SelfPrompting(SolverEval):
+    def __init__(
+        self,
+        completion_fns: list[CompletionFn],
+        samples_jsonl: str,
+        tasker_models: list[str],
+        n_tasks: int = 50,
+        n_samples_per_task: int = 10,
+        n_preview_samples: int = 5,
+        baseline_logpath: Optional[str] = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(completion_fns, *args, **kwargs)
+        # CI doesn't have access to model APIs, so replace tasker_models with dummy models
+        # if we're running in CI (i.e. if the first completion_fn is a DummyCompletionFn)
+        if isinstance(completion_fns[0], evals.api.DummyCompletionFn):
+            tasker_models = ["dummy" for _ in tasker_models]
+
+        self.samples_jsonl = samples_jsonl
+        self.tasker_models = tasker_models
+        self.n_tasks = n_tasks
+        self.n_samples_per_task = n_samples_per_task
+        self.n_preview_samples = n_preview_samples
+        self.baseline_logpath = (
+            self._prefix_registry_path(baseline_logpath) if baseline_logpath else None
+        )
+        assert len(self.tasker_models) > 0, "Must provide at least one tasker model"
+        assert self.n_tasks > 0, "Must provide at least one task"
+        assert self.n_samples_per_task > 0, "Must provide at least one sample per task"
+
+        np.random.seed(self.seed)
+
+        self.tasker_completion_fns = {}
+        for tasker_model in self.tasker_models:
+            self.tasker_completion_fns[tasker_model] = registry.make_completion_fn(tasker_model)
+
+    def eval_sample(self, solver: Solver, sample: Any, rng: random.Random):
+        if sample["stage"] == "prompting":
+            return self._run_prompting(solver, sample)
+        elif sample["stage"] == "tasking":
+            return self._run_tasking(sample)
+        else:
+            raise ValueError(f"Invalid stage {sample['stage']}")
+
+    def _run_prompting(self, solver: Solver, sample: Any, *_):
+        # Prompt the prompter_model to generate a prompt for the tasker_model
+        task_description = task_description_template.format(
+            instruction=sample["task"]["instruction"],
+            samples=json.dumps(sample["task"]["train_samples"], indent=2),
+            tasker_model=sample["tasker_model"],
+        )
+        task_state = TaskState(
+            task_description=task_description,
+            current_state={
+                "instruction": sample["task"]["instruction"],
+                "samples": sample["task"]["train_samples"],
+                "tasker_model": sample["tasker_model"],
+            },
+        )
+        solver_result = solver(task_state)
+        model_instruction = solver_result.output
+
+        prompt_rule_violation = sample_in_token not in model_instruction
+
+        output = {
+            **sample,
+            "task_description": task_description,
+            "current_state": task_state.current_state,
+            "prompting_solver_metadata": solver_result.to_json(),
+            "model_instruction": model_instruction,
+            "prompt_rule_violation": prompt_rule_violation,
+        }
+        return output
+
+    def _run_tasking(self, sample: Any, *_):
+        tasker_completion_fn = self.tasker_completion_fns[sample["tasker_model"]]
+
+        if sample_in_token in sample["model_instruction"]:
+            # Fill in the sample input
+            full_prompt = sample["model_instruction"].replace(sample_in_token, sample["input"])
+        else:
+            # Append the sample input
+            full_prompt = f"{sample['model_instruction']}\n{sample['input']}"
+        tasker_output = tasker_completion_fn(full_prompt).get_completions()[0]
+
+        exact = 1 if tasker_output == sample["output"] else 0
+        fuzzy = 1 if tasker_output in sample["output"] or sample["output"] in tasker_output else 0
+
+        output = {
+            **sample,
+            "full_prompt": full_prompt,
+            "tasker_output": tasker_output,
+            "exact": exact,
+            "fuzzy": fuzzy,
+        }
+        evals.record.record_metrics(**output)
+        return output
+
+    def _calculate_improvement_wrt_baseline(
+        self, current_res: dict[str, float]
+    ) -> dict[str, float]:
+        if self.baseline_logpath is None:
+            logger.warn("SKIPPING IMPROVEMENT METRICS. (No baseline logpath provided.)")
+            return {}
+
+        # Check that baseline was run on the same tasker models, tasks, and samples
+        baseline_spec = extract_spec(Path(self.baseline_logpath))
+        try:
+            spec_args = baseline_spec["run_config"]["eval_spec"]["args"]
+        except KeyError:
+            logger.warn("SKIPPING IMPROVEMENT METRICS. (Failed to validate baseline spec.)")
+            return {}
+        if set(spec_args["tasker_models"]) != set(self.tasker_models):
+            logger.warn(
+                f"SKIPPING IMPROVEMENT METRICS. (Baseline tasker_models {spec_args['tasker_models']} do not match {self.tasker_models}.)"
+            )
+            return {}
+        if (
+            spec_args["n_tasks"] != self.n_tasks
+        ):  # TODO: Ideally we would check that the tasks are the same
+            logger.warn(
+                f"SKIPPING IMPROVEMENT METRICS. (Baseline n_tasks {spec_args['n_tasks']} does not match {self.n_tasks}.)"
+            )
+            return {}
+        if spec_args["n_samples_per_task"] != self.n_samples_per_task:
+            logger.warn(
+                f"SKIPPING IMPROVEMENT METRICS. (Baseline n_samples_per_task {spec_args['n_samples_per_task']} does not match {self.n_samples_per_task}.)"
+            )
+            return {}
+
+        baseline_res = extract_final_results(Path(self.baseline_logpath))
+
+        def normalized_improvement(current, baseline):
+            """
+            Returns a score between -1 and 1, where
+            -1 means the current score maximally regresses from the baseline (i.e. the current score is 0)
+            0 means the current score is the same as the baseline
+            +1 means the current score achieves max improvement over the baseline
+            """
+            if current < baseline:
+                return (current - baseline) / baseline
+            else:
+                return (current - baseline) / (1 - baseline)
+
+        improvement_scores = {
+            "accuracy_improvement_wrt_oriprompt": normalized_improvement(
+                current_res["accuracy"], baseline_res["accuracy"]
+            ),
+            "accuracy_fuzzy_improvement_wrt_oriprompt": normalized_improvement(
+                current_res["accuracy_fuzzy"], baseline_res["accuracy_fuzzy"]
+            ),
+            "baseline_accuracy": baseline_res["accuracy"],
+            "baseline_accuracy_fuzzy": baseline_res["accuracy_fuzzy"],
+        }
+        logger.info(f"Improvement scores: {improvement_scores}")
+        return improvement_scores
+
+    def run(self, recorder: evals.record.Recorder) -> dict[str, Union[float, int]]:
+        samples = self.get_samples()
+
+        # Shuffle and limit samples
+        np.random.shuffle(samples)
+        samples_by_task = samples[: self.n_tasks]
+        assert len(samples_by_task) == self.n_tasks
+        for task in samples_by_task:
+            np.random.shuffle(task["test_samples"])
+            np.random.shuffle(task["train_samples"])
+            task["test_samples"] = task["test_samples"][: self.n_samples_per_task]
+            task["train_samples"] = task["train_samples"][: self.n_preview_samples]
+            assert len(task["test_samples"]) == self.n_samples_per_task
+            assert len(task["train_samples"]) == self.n_preview_samples
+
+        # Run prompting
+        prompting_samples = []
+        for task in samples_by_task:
+            for tasker_model in self.tasker_models:
+                prompting_samples.append(
+                    {
+                        "stage": "prompting",
+                        "tasker_model": tasker_model,
+                        "task": task,
+                    }
+                )
+        assert len(prompting_samples) == len(self.tasker_models) * self.n_tasks
+        prompting_results = self.eval_all_samples(recorder, prompting_samples)
+
+        # Run tasking
+        tasking_samples = []  # Store in flattened list for parallel eval
+        for prompt_res in prompting_results:
+            prompt_res["stage"] = "tasking"  # Update stage
+            for sample in prompt_res["task"]["test_samples"]:
+                tasking_samples.append(
+                    {
+                        **prompt_res,
+                        "input": sample["input"],
+                        "output": sample["output"],
+                    }
+                )
+        assert len(tasking_samples) == len(prompting_results) * self.n_samples_per_task
+        self.eval_all_samples(recorder, tasking_samples)
+
+        # The score of a Prompter is the average score of all Tasker models it writes prompts for
+        metrics = recorder.get_metrics()
+
+        # Primary metrics
+        result = {
+            "accuracy": np.mean([metric["exact"] for metric in metrics]),
+            "accuracy_fuzzy": np.mean([metric["fuzzy"] for metric in metrics]),
+        }
+        # Relative improvement against baseline
+        improvement_scores = self._calculate_improvement_wrt_baseline(result)
+        if improvement_scores:
+            result.update(improvement_scores)
+
+        # Peripheral metrics
+        result.update(
+            {
+                "prompt_rule_violation_rate": np.mean(
+                    [int(metric["prompt_rule_violation"]) for metric in metrics]
+                ),
+                "n_samples": len(metrics),
+            }
+        )
+
+        # Breakdown by tasker model
+        def compute_mean_tasker(key, tasker_model):
+            return np.mean(
+                [metric[key] for metric in metrics if metric["tasker_model"] == tasker_model]
+            )
+
+        for tasker in self.tasker_models:
+            result.update(
+                {
+                    f"accuracy_{tasker}": compute_mean_tasker("exact", tasker),
+                    f"accuracy_fuzzy_{tasker}": compute_mean_tasker("fuzzy", tasker),
+                }
+            )
+
+        return result

evals/elsuite/self_prompting/readme.md

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+# Eval description
+
+How well can LMs write prompts for themselves to perform various tasks?
+
+In the Self-Prompting eval, models (Prompters) write prompts for other models (Taskers) to perform various tasks -- which are other evals from this repository (listed below). Prompters are given an initial human-written prompt for the task, and asked to rewrite it for a given Tasker model. The effectiveness of the Prompters are measured in terms of the accuracy of downstream Taskers on the tasks. We measure this prompting ability for a variety of different downstream models: gpt-3.5-turbo, gpt-4-base, and gpt-4.
+
+The headline metric for a Prompter’s success is the mean accuracy of the predictions of all its Taskers on all tasks.
+- For our primary metric `accuracy`, the accuracy score uses an exact match criterion to judge if the tasker response is correct or not (a response is correct if and only if it exactly matches the true label in the dataset).
+- As a secondary metric `accuracy_fuzzy`, we also compute results with a fuzzy match criterion, which counts a response as correct if either the model response contains the label or the label contains the response.
+
+Additionally, we also present `accuracy_improvement_wrt_oriprompt` and `accuracy_fuzzy_improvement_wrt_oriprompt` which are the accuracies normalized relative to the score of the original prompt baseline. This is a score between -1 and +1, where -1 means the current score maximally regresses from the baseline (i.e. the current score is 0), 0 means the current score is the same as the baseline, and +1 means the current score achieves max improvement over the baseline. By default, the baseline score is a cached score of the original prompt (`self_prompting/oriprompt/baseline`) on the `self_prompting.full` eval.
+
+# Usage
+
+To run the eval, use the following command:
+```bash
+oaieval {solver} self_prompting
+```
+where `{solver}` is the name of the solver you want to evaluate, e.g. `self_prompting/chat_completion/gpt-4-32k`.
+
+# Experiments
+As a starting point for deeper exploration, we provide scripts for comparing various solvers and eval variants, as well as for plotting the results. To run these:
+```
+cd scripts/
+bash run_experiments.sh
+```
+
+# Dataset
+
+To form the self-prompting dataset, we extract tasks from this `evals` repository, selecting for datasets with
+1. A system prompt that can be straightforwardly converted into a generic instruction for all task samples
+2. A straightforward input-output format for each task sample.
+3. Designed to be evaluated with an exact match criterion.
+
+The full list of 50 evals we use can be found in `scripts/dataset/eval_list.py`.
+
+# Token estimate
+Below, we present a rough estimate of the total number of tokens consumed by the eval, including both input and output tokens.
+
+For self-prompting, each eval run queries multiple models. In the following table, we present the number of tokens consumed by Prompter models:
+
+| Model             | Solver type     | Tokens  |
+|-------------------|-----------------|---------|
+| code-davinci-002  | completion_hhh  | 400 000 |
+| gpt-4-base        | completion_hhh  | 360 000 |
+| gpt-3.5-turbo-16k | chat_completion | 180 000 |
+| gpt-4-32k         | chat_completion | 155 000 |
+| gpt-3.5-turbo-16k | cot             | 480 000 |
+| gpt-4-32k         | cot             | 420 000 |
+| gpt-3.5-turbo-16k | cotexpert       | 495 000 |
+| gpt-4-32k         | cotexpert       | 450 000 |
+
+In addition to the Prompter tokens, each run also queries multiple Tasker models. By default, we use gpt-3.5-turbo, gpt-4-base, and gpt-4, consuming an additional 100k-200k tokens per model.
+
+To calculate dollar cost from token counts, please check the latest token pricing [here](https://openai.com/pricing). Note that we count both input and output tokens together, so a lower and upper estimate of the cost of each variant can be predicted.
+
+# Contribution statement
+Eval design, implementation, and results evaluation were primarily conducted by Chan Jun Shern under the guidance of (alphabetically by last-name) Steven Adler, James Aung, Rosie Campbell, and Jade Leung, who provided research input and project management support.