From f57cbe077d97f825da356ccbf3ce05e6c0575fce Mon Sep 17 00:00:00 2001
From: Maximilian Kimmich <maximilian.kimmich@ims.uni-stuttgart.de>
Date: Thu, 10 Oct 2024 16:12:30 +0200
Subject: [PATCH] Use chat format for evolution demonstration samples
---
evoprompt/evolution/evolution.py | 259 +++++++++++++++++++++----------
evoprompt/evolution/template.py | 6 -
2 files changed, 173 insertions(+), 92 deletions(-)
delete mode 100644 evoprompt/evolution/template.py
diff --git a/evoprompt/evolution/evolution.py b/evoprompt/evolution/evolution.py
index ce57de5..b5d71f9 100644
--- a/evoprompt/evolution/evolution.py
+++ b/evoprompt/evolution/evolution.py
@@ -1,3 +1,4 @@
+from collections.abc import Iterable
import logging
import re
from abc import ABCMeta, abstractmethod
@@ -7,7 +8,6 @@ from tqdm import trange
import wandb
import weave
-from evoprompt.evolution.template import get_demonstration_prompt_template
from evoprompt.evolution.template_de import (
DE_DEMONSTRATION_DATA_CLS,
DE_DEMONSTRATION_DATA_SIM,
@@ -91,6 +91,42 @@ class EvolutionAlgorithm(PromptOptimization, metaclass=ABCMeta):
) -> tuple[str, list[Judgement], ModelUsage]:
pass
+ def build_demonstration_prompt(
+ self,
+ demonstration_samples: Iterable[tuple[str, str]],
+ instruction: str = None,
+ ) -> ChatMessages:
+ return self.evolution_model.build_demonstration_data(
+ demonstration_samples,
+ instruction=instruction,
+ )
+
+ def parse_response(
+ self,
+ response: str,
+ start_token: str = "<prompt>",
+ end_token: str = "</prompt>",
+ allow_missing_end_token: bool = True,
+ ):
+ matches = re.findall(
+ # regex that matches any characters between last pair of `start_token` and `end_token`, and optionally allow missing `end_token`
+ rf"{start_token}(?!.*{start_token})(?:(.*){end_token}{"|(.*)" if allow_missing_end_token else ""})",
+ response,
+ flags=(re.IGNORECASE | re.DOTALL),
+ )
+ if matches and any(matches[0]):
+ # there is always only a single match, and one group should be non-empty
+ if matches[0][0]:
+ evolved_prompt = matches[0][0]
+ else:
+ assert matches[0][1]
+ evolved_prompt = matches[0][1]
+
+ else:
+ # could not extract prompt -> no new prompt
+ evolved_prompt = None
+ return evolved_prompt
+
@abstractmethod
def update(self, *args, **kwargs):
pass
@@ -245,20 +281,23 @@ class GeneticAlgorithm(EvolutionAlgorithm):
# Based on this two-step process, we design instructions, guiding LLMs to
# generate a new prompt based on these steps to perform Evo(·) in Algorithm 1.
- filled_prompt = self.get_prompt_template().format(
+ prompt = self._get_prompt_template()
+ if isinstance(prompt, tuple) and len(prompt) > 1:
+ # extract demonstrations
+ prompt, demo_messages = prompt
+ filled_prompt = prompt.format(
prompt1=prompt_1,
prompt2=prompt_2,
)
- evolved_prompt, history, recent_turn, usage = (
- self.evolution_model.create_completion(
- system_message=SYSTEM_MESSAGE,
- prompt=filled_prompt,
- use_randomness=True,
- )
+ response, history, recent_turn, usage = self.evolution_model.create_completion(
+ system_message=SYSTEM_MESSAGE,
+ prompt=filled_prompt,
+ history=demo_messages if self.use_evolution_demo else None,
+ use_randomness=True,
)
judgement = self.judge_and_correct_step(
- filled_prompt, evolved_prompt, history, recent_turn
+ filled_prompt, response, history, recent_turn
)
if judgement.skip:
@@ -269,17 +308,18 @@ class GeneticAlgorithm(EvolutionAlgorithm):
usage,
)
- evolved_prompt = judgement.corrected_response
-
- if "<prompt>" in evolved_prompt:
- evolved_prompt = evolved_prompt.split("<prompt>")[1].split("</prompt>")[0]
+ response = judgement.corrected_response
+ evolved_prompt = self.parse_response(response)
- logger.info(
- "GA-evolved prompts '%s' and '%s' into '%s'",
- prompt_1,
- prompt_2,
- evolved_prompt,
- )
+ if evolved_prompt is None:
+ logger.info(f"Could not extract prompt from response: {evolved_prompt}")
+ else:
+ logger.info(
+ "GA-evolved prompts '%s' and '%s' into '%s'",
+ prompt_1,
+ prompt_2,
+ evolved_prompt,
+ )
return evolved_prompt, [judgement], usage
@@ -305,22 +345,28 @@ class GeneticAlgorithm(EvolutionAlgorithm):
return retained_prompts
- def get_prompt_template(self):
+ def _get_prompt_template(self):
if self.use_evolution_demo:
if isinstance(
self.task, (TextClassification, Summarization, QuestionAnswering)
):
- return get_demonstration_prompt_template(
- GA_PROMPT, GA_DEMONSTRATION_DATA_SIM
- )
+ demonstration_data = GA_DEMONSTRATION_DATA_SIM
elif isinstance(self.task, Simplification):
- return get_demonstration_prompt_template(
- GA_PROMPT, GA_DEMONSTRATION_DATA_CLS
- )
+ demonstration_data = GA_DEMONSTRATION_DATA_CLS
else:
raise NotImplementedError(
f"Prompt with demonstration data is not implemented for task of type {type(self.task)}."
)
+
+ prompt_with_demonstration_data = self.build_demonstration_prompt(
+ [
+ (
+ GA_PROMPT.format(**demonstration_data),
+ demonstration_data["response"],
+ )
+ ]
+ )
+ return GA_PROMPT, prompt_with_demonstration_data
return GA_PROMPT
@@ -346,22 +392,25 @@ class DifferentialEvolution(EvolutionAlgorithm):
prompts_current_evolution, key=lambda prompt: prompt.score
)
- filled_prompt = self.get_prompt_template().format(
+ prompt = self._get_prompt_template()
+ if isinstance(prompt, tuple) and len(prompt) > 1:
+ # extract demonstrations
+ prompt, demo_messages = prompt
+ filled_prompt = prompt.format(
prompt1=prompt_1,
prompt2=prompt_2,
prompt3=best_prompt_current_evolution,
basic_prompt=prompts_current_evolution[current_iteration],
)
- evolved_prompt, history, recent_turn, usage = (
- self.evolution_model.create_completion(
- system_message=SYSTEM_MESSAGE,
- prompt=filled_prompt,
- use_randomness=True,
- )
+ response, history, recent_turn, usage = self.evolution_model.create_completion(
+ system_message=SYSTEM_MESSAGE,
+ prompt=filled_prompt,
+ history=demo_messages if self.use_evolution_demo else None,
+ use_randomness=True,
)
judgement = self.judge_and_correct_step(
- filled_prompt, evolved_prompt, history, recent_turn
+ filled_prompt, response, history, recent_turn
)
if judgement.skip:
@@ -372,33 +421,20 @@ class DifferentialEvolution(EvolutionAlgorithm):
usage,
)
- evolved_prompt = judgement.corrected_response
+ response = judgement.corrected_response
+ evolved_prompt = self.parse_response(response)
- matches = re.findall(
- # regex that matches any characters between last pair of <prompt></prompt>, also if </prompt> is missing
- r"<prompt>(?!.*<prompt>)(?:(.*)</prompt>|(.*))",
- evolved_prompt,
- flags=(re.IGNORECASE | re.DOTALL),
- )
- if matches and any(matches[0]):
- # there is always only a single match, and one group should be non-empty
- if matches[0][0]:
- evolved_prompt = matches[0][0]
- else:
- assert matches[0][1]
- evolved_prompt = matches[0][1]
+ if evolved_prompt is None:
+ logger.info(f"Could not extract prompt from response: {evolved_prompt}")
else:
- # TODO what to do in this case? Discard generated prompt directly?
- pass
-
- logger.info(
- "DE-evolved prompts '%s', '%s' and '%s' with basic prompt '%s' into '%s'",
- prompt_1,
- prompt_2,
- best_prompt_current_evolution,
- prompts_current_evolution[current_iteration],
- evolved_prompt,
- )
+ logger.info(
+ "DE-evolved prompts '%s', '%s' and '%s' with basic prompt '%s' into '%s'",
+ prompt_1,
+ prompt_2,
+ best_prompt_current_evolution,
+ prompts_current_evolution[current_iteration],
+ evolved_prompt,
+ )
return evolved_prompt, [judgement], usage
@@ -416,22 +452,28 @@ class DifferentialEvolution(EvolutionAlgorithm):
]
return population
- def get_prompt_template(self):
+ def _get_prompt_template(self):
if self.use_evolution_demo:
if isinstance(
self.task, (TextClassification, Summarization, QuestionAnswering)
):
- return get_demonstration_prompt_template(
- DE_PROMPT, DE_DEMONSTRATION_DATA_SIM
- )
+ demonstration_data = DE_DEMONSTRATION_DATA_SIM
elif isinstance(self.task, Simplification):
- return get_demonstration_prompt_template(
- DE_PROMPT, DE_DEMONSTRATION_DATA_CLS
- )
+ demonstration_data = DE_DEMONSTRATION_DATA_CLS
else:
raise NotImplementedError(
f"Prompt with demonstration data is not implemented for task of type {type(self.task)}."
)
+
+ prompt_with_demonstration_data = self.build_demonstration_prompt(
+ [
+ (
+ DE_PROMPT.format(**demonstration_data),
+ demonstration_data["response"],
+ )
+ ]
+ )
+ return DE_PROMPT, prompt_with_demonstration_data
return DE_PROMPT
@@ -457,27 +499,46 @@ class DifferentialEvolutionWithCot(DifferentialEvolution):
prompts_current_evolution, key=lambda prompt: prompt.score
)
- history = None
+ # list of evolution steps
+ evolutions_steps = []
+ # list (turns) of list (demonstrations)
+ demos = [[]]
response: str = ""
judgements: list[Judgement] = []
usage: ModelUsage = ModelUsage()
- for idx, prompt in enumerate(self.get_prompt_template()):
+ for idx, prompt in enumerate(self._get_prompt_template()):
+ if isinstance(prompt, tuple) and len(prompt) > 1:
+ # extract demonstrations
+ prompt, demo_messages = prompt
+ demos[-1].extend(demo_messages)
+ if self.use_evolution_demo:
+ messages_demos = self.condense_messages(demos[-1])
+ else:
+ messages_demos = []
filled_prompt = prompt.format(
prompt1=prompt_1,
prompt2=prompt_2,
prompt3=best_prompt_current_evolution,
basic_prompt=prompts_current_evolution[current_iteration],
)
+ evolutions_steps.append(
+ self.evolution_model._get_user_message(filled_prompt)
+ )
+ # TODO Shall we still use only a single turn containing all messages if we do not use demonstrations for evolution?
+ messages = self.condense_messages(evolutions_steps)
response, history, recent_turn, usage = (
self.evolution_model.create_completion(
system_message=SYSTEM_MESSAGE,
- prompt=filled_prompt,
- history=history,
+ messages=messages,
+ history=messages_demos,
# the models often repeat the instuction which could also contain </prompt> therefore we should not stop early
- stop=None, # "</prompt>" if idx == len(DE_COT_PROMPTS) - 1 else None,
+ stop=None,
use_randomness=True,
)
)
+ evolutions_steps.append(
+ self.evolution_model._get_assistant_message(response)
+ )
logger.debug(
"Performed evolution (step %d) using DE-CoT:\n\tInputs: %s\n\tResponse: %s",
idx,
@@ -504,22 +565,39 @@ class DifferentialEvolutionWithCot(DifferentialEvolution):
# update history with recent turn
history += recent_turn
- # at this point we should get a new prompt
- if "<prompt>" in response:
- response = response.split("<prompt>")[1].split("</prompt>")[0]
+ evolved_prompt = self.parse_response(response)
- logger.info(
- "DE-CoT-evolved prompts '%s', '%s' and '%s' with basic prompt '%s' into '%s'",
- prompt_1,
- prompt_2,
- best_prompt_current_evolution,
- prompts_current_evolution[current_iteration],
- response,
- )
+ if evolved_prompt is None:
+ logger.info(f"Could not extract prompt from response: {evolved_prompt}")
+ else:
+ logger.info(
+ "DE-evolved prompts '%s', '%s' and '%s' with basic prompt '%s' into '%s'",
+ prompt_1,
+ prompt_2,
+ best_prompt_current_evolution,
+ prompts_current_evolution[current_iteration],
+ evolved_prompt,
+ )
+
+ return evolved_prompt, judgements, usage
+
+ def condense_messages(self, messages: list[ChatMessages]) -> list[dict]:
+ if not messages:
+ return []
- return response, judgements, usage
+ if messages[-1]["role"] == "assistant":
+ assistant_turn = messages[-1]
+ messages = messages[:-1]
+ else:
+ assistant_turn = None
+
+ user_turn = "\n\n".join(message["content"] for message in messages)
+ messages = [self.evolution_model._get_user_message(user_turn)]
+ if assistant_turn is not None:
+ messages.append(assistant_turn)
+ return messages
- def get_prompt_template(self):
+ def _get_prompt_template(self):
if self.use_evolution_demo:
if isinstance(
self.task, (TextClassification, Summarization, QuestionAnswering)
@@ -532,9 +610,18 @@ class DifferentialEvolutionWithCot(DifferentialEvolution):
f"Prompt with demonstration data is not implemented for task of type {type(self.task)}."
)
- for prompt, demonstration_data_item in zip(
+ for prompt_template, demonstration_data_item in zip(
DE_COT_PROMPTS, demonstration_data
):
- yield get_demonstration_prompt_template(prompt, demonstration_data_item)
+ prompt_with_demonstration_data = self.build_demonstration_prompt(
+ [
+ (
+ prompt_template.format(**demonstration_data_item),
+ demonstration_data_item["response"],
+ )
+ ]
+ )
+ yield prompt_template, prompt_with_demonstration_data
+ # TODO how can we add a generation prefix for the model?
else:
yield from DE_COT_PROMPTS
diff --git a/evoprompt/evolution/template.py b/evoprompt/evolution/template.py
deleted file mode 100644
index a84dbb3..0000000
--- a/evoprompt/evolution/template.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def get_demonstration_prompt_template(prompt_template: str, demonstration_data: dict):
- prompt_template_with_demo = prompt_template.format(**demonstration_data)
- prompt_template_with_demo += "\n\n" + demonstration_data["response"]
- prompt_template_with_demo += "\n\n" + prompt_template
- prompt_template_with_demo += "\n\n" + demonstration_data["generation_prefix"]
- return prompt_template_with_demo
--
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