diff --git a/evoprompt/evolution/evolution.py b/evoprompt/evolution/evolution.py
index 76a82755c4bf55982260d328260a41226b1da861..627b1ea4b87e77dd23b4d1e4247b30e2ffefe002 100644
--- a/evoprompt/evolution/evolution.py
+++ b/evoprompt/evolution/evolution.py
@@ -556,79 +556,100 @@ class EvolutionAlgorithm(PromptOptimization, metaclass=ABCMeta):
with weave.attributes({"run_name": self.run_name}):
# Algorithm 1 Discrete prompt optimization: EVOPROMPT
-
# Line 2:
for t in self.iterations_pbar:
# Line 3: Selection: select a certain number of prompts from current population as parent prompts
# pr1,...,prk ∼ Pt−1
- prompts_current_evolution = self.P[t - 1]
-
- new_evolutions = []
- num_failed_automatic_evolutions = 0
- for i in trange(
- self.population_size,
- desc="updates",
- leave=False,
- disable=None,
+ # if the iteration does not improve over the previous generation, we rerun the iteration with human feedback
+ # TODO: what to do if the iteration does not improve over the previous generation even with human feedback?
+ # this design allows us to introduce a `max_human_feedback_iterations` parameter if needed
+ iteration_try_for_generation = 0
+ iteration_has_improved = False
+ while (
+ not iteration_has_improved
+ and iteration_try_for_generation < 2
):
- # for both GA and DE we start with two parent prompts
- pr1, pr2 = self.select(self.P[t - 1])
+ prompts_current_evolution = self.P[t - 1]
+
+ new_evolutions = []
+ num_failed_automatic_evolutions = 0
+ for i in trange(
+ self.population_size,
+ desc="updates",
+ leave=False,
+ disable=None,
+ ):
+ # for both GA and DE we start with two parent prompts
+ pr1, pr2 = self.select(self.P[t - 1])
+
+ # Line 4: Evolution: generate a new prompt based on the selected parent prompts by leveraging LLM to perform evolutionary operation
+ # p′i â†Evo(pr1,...,prk)
+ (
+ evolved_prompt,
+ judgements,
+ evolution_usage,
+ ) = self.evolve(
+ pr1,
+ pr2,
+ prompts_current_evolution=prompts_current_evolution,
+ current_iteration=i,
+ current_generation=t,
+ )
+ self.total_evolution_usage += evolution_usage
- # Line 4: Evolution: generate a new prompt based on the selected parent prompts by leveraging LLM to perform evolutionary operation
- # p′i â†Evo(pr1,...,prk)
- (
- evolved_prompt,
- judgements,
- evolution_usage,
- ) = self.evolve(
- pr1,
- pr2,
+ automatic_prompt_evolution_failed = bool(
+ {Judgement.BAD, Judgement.FAIL} & set(judgements)
+ )
+ if automatic_prompt_evolution_failed:
+ num_failed_automatic_evolutions += 1
+ # If a prompt is None, it means that the prompt was skipped
+ if evolved_prompt is not None:
+ prompt_source = (
+ "corrected" # could also mean that user skipped the prompt
+ if automatic_prompt_evolution_failed
+ else "evolution"
+ )
+
+ evolved_prompt = self.add_prompt(
+ evolved_prompt,
+ parents=(pr1, pr2),
+ meta={
+ "gen": t,
+ "source": prompt_source,
+ "judgements": judgements,
+ },
+ )
+ self.total_evaluation_usage += evolved_prompt.usage
+ self.log_prompt(
+ evolved_prompt,
+ generation=t,
+ update_step=i,
+ total_update_step=t * self.population_size + i,
+ )
+
+ new_evolutions.append(evolved_prompt)
+ else:
+ new_evolutions.append(None)
+ self.save_snapshot()
+ # Line 6: Update based on the evaluation scores
+ # Pt ↠{Pt−1, p′i} and St ↠{St−1, s′i}
+ new_population = self.update(
+ new_evolutions=new_evolutions,
prompts_current_evolution=prompts_current_evolution,
- current_iteration=i,
- current_generation=t,
)
- self.total_evolution_usage += evolution_usage
- automatic_prompt_evolution_failed = bool(
- {Judgement.BAD, Judgement.FAIL} & set(judgements)
+ best_score = lambda population: max(
+ population, key=lambda prompt: prompt.score
)
- if automatic_prompt_evolution_failed:
- num_failed_automatic_evolutions += 1
- # If a prompt is None, it means that the prompt was skipped
- if evolved_prompt is not None:
- prompt_source = (
- "corrected" # could also mean that user skipped the prompt
- if automatic_prompt_evolution_failed
- else "evolution"
- )
- evolved_prompt = self.add_prompt(
- evolved_prompt,
- parents=(pr1, pr2),
- meta={
- "gen": t,
- "source": prompt_source,
- "judgements": judgements,
- },
+ iteration_has_improved = best_score(
+ new_population
+ ) > best_score(prompts_current_evolution)
+ if not iteration_has_improved:
+ logger.info(
+ "Iteration %d did not improve over previous generation. Rerunning with human feedback",
+ t,
)
- self.total_evaluation_usage += evolved_prompt.usage
- self.log_prompt(
- evolved_prompt,
- generation=t,
- update_step=i,
- total_update_step=t * self.population_size + i,
- )
-
- new_evolutions.append(evolved_prompt)
- else:
- new_evolutions.append(None)
- self.save_snapshot()
- # Line 6: Update based on the evaluation scores
- # Pt ↠{Pt−1, p′i} and St ↠{St−1, s′i}
- new_population = self.update(
- new_evolutions=new_evolutions,
- prompts_current_evolution=prompts_current_evolution,
- )
# log metrics to wandb
self.log_iteration(