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import re
import time
import traceback
from copy import deepcopy
from typing import List
from slime.rollout.rm_hub import batched_async_rm
from slime.utils.http_utils import post
from slime.utils.types import Sample
from .prompts import SOLVER_PROMPT_TEMPLATE, generate_rewriter_template, generate_select_template
async def generate_response(args, prompt, key):
try:
sampling_params = args.sampling_params
tokenizer = args.tokenizer
max_context_length = args.rollout_max_context_len
sample = deepcopy(args.sample)
url = f"http://{args.sglang_router_ip}:{args.sglang_router_port}/generate"
prompt_token_ids = tokenizer.encode(prompt, add_special_tokens=False)
sample.tokens = prompt_token_ids
sample.prompt = prompt
input_token_ids = prompt_token_ids
prompt_length = len(input_token_ids)
current_sampling_params = deepcopy(sampling_params)
current_sampling_params["max_new_tokens"] = min(
sampling_params["max_new_tokens"], max_context_length - prompt_length
)
if current_sampling_params["max_new_tokens"] <= 0:
return None
payload = {"input_ids": input_token_ids, "sampling_params": current_sampling_params, "return_logprob": True}
output = await post(url, payload)
# Extract new response tokens
if "output_token_logprobs" in output["meta_info"]:
new_response_tokens = [item[1] for item in output["meta_info"]["output_token_logprobs"]]
else:
# abort
new_response_tokens = []
# Update sample with tokens directly - avoiding re-tokenization
sample.tokens = sample.tokens + new_response_tokens
sample.response_length += len(new_response_tokens)
sample.response = output["text"]
match output["meta_info"]["finish_reason"]["type"]:
case "length":
sample.status = Sample.Status.TRUNCATED
# case "abort":
# sample.status = Sample.Status.ABORTED
case "stop":
sample.status = Sample.Status.COMPLETED
args.results_dict[key].append(sample)
final = output["text"].replace("<|user|>", "")
if "</think>" in final:
contents = final.split("</think>")
if len(contents) == 2 and contents[1] != "":
reason_content = contents[0].strip()
response_content = contents[1].strip()
sample.reason_content = reason_content
sample.response_content = response_content
return response_content
sample.reason_content = None
sample.response_content = None
return None
except Exception as e:
print(f"Error generating response: {e}")
return None
class Agent:
"""A base class for our AI agents."""
def __init__(self):
pass
async def run(self, args, prompt, max_retries: int = 1, key: str = None) -> str:
"""Runs the agent by sending a prompt to the LLM."""
for i in range(max_retries):
try:
response = await generate_response(args, prompt, key=key)
return response
except Exception as e:
print(f"Error querying LLM: {e}")
time.sleep(1)
print(f"Failed to query LLM after {max_retries} retries")
return None
class SolverAgent(Agent):
"""The agent responsible for generating and improving solutions."""
def __init__(self):
super().__init__()
async def generate_initial_solution(self, args, problem_statement) -> str:
"""Generates the first solution attempt."""
prompt = SOLVER_PROMPT_TEMPLATE.format(problem_statement=problem_statement)
return await self.run(args, prompt, max_retries=3, key="solver")
class RewriterAgent(Agent):
"""The agent responsible for rewriting solutions."""
def __init__(self):
super().__init__()
async def rewrite(self, args, problem_statement, previous_solutions: List[str]) -> str:
"""Generates the rewrited solution."""
# Dynamically generate template
template = generate_rewriter_template(len(previous_solutions))
# Build parameter dictionary
format_params = {"problem_statement": problem_statement}
for i, solution in enumerate(previous_solutions):
format_params[f"solution{i+1}"] = solution
prompt = template.format(**format_params)
return await self.run(args, prompt, max_retries=1, key="rewriter")
class SelectorAgent(Agent):
"""The agent responsible for selecting solutions."""
def __init__(self):
super().__init__()
async def select(self, args, problem_statement, candidate_solutions: List[str]) -> str:
"""Generates the rewrited solution."""
# Dynamically generate template
template = generate_select_template(len(candidate_solutions))
# Build parameter dictionary
format_params = {"problem_statement": problem_statement}
for i, solution in enumerate(candidate_solutions):
format_params[f"solution{i+1}"] = solution
prompt = template.format(**format_params)
return await self.run(args, prompt, max_retries=10, key="selector")
def extract_selected_solution_idx(self, response: str, candidate_solutions: List[str]) -> int:
"""Extracts the selected solution ID from the response."""
PATTERN = re.compile("Judgment:\s*(\d+)")
matched = PATTERN.findall(response)
try:
selected_id = int(matched[0]) - 1
if selected_id < len(candidate_solutions) and selected_id >= 0:
return selected_id
else:
return None
except Exception as e:
print(f"extract_selected_solution_idx error: {e}")
return None
async def rewrite_worker(args, previous_solutions, problem_statement, worker_id):
rewriter = RewriterAgent()
new_solution = await rewriter.rewrite(args, problem_statement, previous_solutions)
return new_solution
async def solver_worker(args, problem_statement, worker_id):
"""
Single solver workflow.
"""
try:
solver = SolverAgent()
current_solution = await solver.generate_initial_solution(args, problem_statement)
return current_solution
except Exception as e:
print(f"[Worker-{worker_id}] exception: {e}")
print(f"[Worker-{worker_id}] traceback: {traceback.format_exc()}")
return None
async def run_agent_system(args, sample):
"""
Concurrently run num_parallel pipeline instances.
"""
args = deepcopy(args) # Deep copy args since it may be modified in rollout_with_multi_agents
args.sample = sample
args.results_dict = {"solver": [], "rewriter": [], "selector": []}
problem_statement = sample.prompt
tasks = [solver_worker(args, problem_statement, worker_id) for worker_id in range(args.num_parallel)]
results = await asyncio.gather(*tasks, return_exceptions=True)
rewards = await batched_async_rm(args, args.results_dict["solver"])
for sample, reward in zip(args.results_dict["solver"], rewards):
sample.reward = reward
previous_solutions = [item for item in results if isinstance(item, str)]
def reward_adjustment(samples, reward_weight):
for sample in samples:
sample.reward = sample.reward * reward_weight
return samples
if len(previous_solutions) == 0:
reward_adjustment(args.results_dict["solver"], args.incorrect_reward_weight)
return args.results_dict["solver"]
# Rewriting
tasks = [
rewrite_worker(args, previous_solutions, problem_statement, worker_id)
for worker_id in range(args.num_parallel)
]
rewrited_solutions_raw = await asyncio.gather(*tasks, return_exceptions=True)
# Handle exception results
rewrited_solutions = []
for i, result in enumerate(rewrited_solutions_raw):
if isinstance(result, str):
rewrited_solutions.append(result)
rewards = await batched_async_rm(args, args.results_dict["rewriter"])
for sample, reward in zip(args.results_dict["rewriter"], rewards):
sample.reward = reward
if len(rewrited_solutions) == 0:
reward_adjustment(args.results_dict["solver"], args.incorrect_reward_weight)
reward_adjustment(args.results_dict["rewriter"], args.incorrect_reward_weight)
return args.results_dict["solver"] + args.results_dict["rewriter"]
# Selection
selector = SelectorAgent()
response = await selector.select(args, problem_statement, rewrited_solutions)
if len(args.results_dict["selector"]) == 0:
reward_adjustment(args.results_dict["solver"], args.incorrect_reward_weight)
reward_adjustment(args.results_dict["rewriter"], args.incorrect_reward_weight)
return args.results_dict["solver"] + args.results_dict["rewriter"]
assert (
len(args.results_dict["selector"]) == 1
), f"selector should only return one solution, but got {len(args.results_dict['selector'])}"
if response is None:
args.results_dict["selector"][0].reward = 0
else:
selected_solution_idx = selector.extract_selected_solution_idx(response, rewrited_solutions)
if selected_solution_idx is None:
args.results_dict["selector"][0].reward = 0
else:
selected_solution = rewrited_solutions[selected_solution_idx]
for sample in args.results_dict["rewriter"]:
if sample.response_content is not None and selected_solution in sample.response_content:
args.results_dict["selector"][0].reward = sample.reward
break
## If final answer is correct, add positive reward to all; if incorrect, add negative penalty to all.
if args.results_dict["selector"][0].reward == 1:
reward_adjustment(args.results_dict["solver"], args.correct_reward_weight)
reward_adjustment(args.results_dict["rewriter"], args.correct_reward_weight)
reward_adjustment(args.results_dict["selector"], args.correct_reward_weight)
else:
reward_adjustment(args.results_dict["solver"], args.incorrect_reward_weight)
reward_adjustment(args.results_dict["rewriter"], args.incorrect_reward_weight)
reward_adjustment(args.results_dict["selector"], args.incorrect_reward_weight)
return args.results_dict["solver"] + args.results_dict["rewriter"] + args.results_dict["selector"]
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