Update Action Graph Solver Version 0.1

examples/ags/benchmark/gsm8k.py

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+# -*- coding: utf-8 -*-
+# @Date    :
+# @Author  : issac
+# @Desc    : test on gsm8k

examples/ags/benchmark/hotpotQA.py

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+# -*- coding: utf-8 -*-
+# @Date    :
+# @Author  : issac
+# @Desc    : test on hotpotqa

examples/ags/benchmark/humaneval.py

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+# -*- coding: utf-8 -*-
+# @Date    : 7/7/2024 17:07 PM
+# @Author  : didi
+# @Desc    : test on human eval graph
+
+import asyncio
+import json
+import os
+import subprocess
+import sys
+from typing import Literal, Optional
+
+import aiofiles
+from evalplus.data import get_human_eval_plus
+
+from examples.ags.w_action_node.graph import HumanEvalGraph
+from examples.ags.w_action_node.operator import GenerateCode, GenerateCodeBlock
+from examples.ags.w_action_node.utils import sort_json_by_task_id
+from metagpt.llm import LLM
+from metagpt.logs import logger
+from metagpt.utils.common import add_jsonl_file, read_json_file
+from metagpt.utils.exceptions import handle_exception
+
+generate_code = GenerateCode(llm=LLM())
+generate_code_block = GenerateCodeBlock(llm=LLM())
+solver = HumanEvalGraph(name="solver", llm=LLM(), criteria="correctness, efficiency, readability", vote_count=5)
+
+ModeType = Literal["ags", "alpha_codium", "llm"]
+
+
+async def llm_generate(id):
+    case = get_human_eval_plus()[f"{id}"]
+    solution_result = await generate_code_block(case["prompt"], case["entry_point"])
+    sample_dict = dict(task_id=case["task_id"], solution=solution_result["code_solution"])
+    return sample_dict
+
+
+async def ags_generate(id, ensemble_count: int = 5):
+    case = get_human_eval_plus()[f"{id}"]
+    solution_result = await solver(case["prompt"], ensemble_count=ensemble_count)
+    sample_dict = dict(task_id=case["task_id"], solution=solution_result["final_solution"])
+    return sample_dict
+
+
+async def alpha_codium_generate(id):
+    case = get_human_eval_plus()[f"{id}"]
+    solution_result = await solver.alpha_codium(case["task_id"], case["prompt"], ensemble_count=5)
+    sample_dict = dict(task_id=case["task_id"], solution=solution_result["final_solution"])
+    return sample_dict
+
+
+async def route_generate(mode: ModeType, id: str):
+    if mode == "ags":
+        sample_dict = await ags_generate(id)
+    elif mode == "alpha_codium":
+        sample_dict = await alpha_codium_generate(id)
+    elif mode == "llm":
+        sample_dict = await llm_generate(id)
+    else:
+        raise ValueError(f"Invalid mode: {mode}")
+    return sample_dict
+
+
+async def sample_generate(id, result_path: str = "samples.jsonl", mode: ModeType = "ags"):
+    sample_dict = await route_generate(mode, id)
+    add_jsonl_file(result_path, [sample_dict])
+    sort_json_by_task_id(result_path, result_path)
+
+
+async def samples_generate(mode: ModeType, result_path: str = "samples.jsonl"):
+    ids = list(get_human_eval_plus().keys())
+    file_lock = asyncio.Lock()
+
+    @handle_exception(
+        exception_type=Exception,
+        exception_msg="Error in solve_and_write function",
+        default_return=lambda id, *args, **kwargs: id,
+    )
+    async def solve_and_write(id: str, mode: ModeType) -> Optional[str]:
+        sample_dict = await route_generate(mode, id)
+        async with file_lock:
+            async with aiofiles.open(result_path, mode="a") as f:
+                await f.write(json.dumps(sample_dict) + "\n")
+        return None
+
+    tasks = [solve_and_write(id, mode) for id in ids]
+    results = await asyncio.gather(*tasks)
+    failed_tasks = [task_id for task_id in results if task_id is not None]
+
+    if failed_tasks:
+        logger.info(failed_tasks)
+        for task_id in failed_tasks:
+            try:
+                await sample_generate(task_id, result_path, mode)
+                failed_tasks.remove(task_id)
+            except Exception:
+                logger.error(f"{task_id} fail")
+
+    sort_json_by_task_id(result_path, result_path)
+
+    if not failed_tasks:
+        if automatic_evalplus(result_path):
+            eval_path = result_path[:-6] + "_eval_results.json"
+            unpassed_exapmle = extract_failure_tests(eval_path)
+            logger.info(unpassed_exapmle)
+    else:
+        logger.info(failed_tasks)
+
+
+@handle_exception(exception_type=subprocess.CalledProcessError, exception_msg="sanitize error", default_return=None)
+def automatic_sanitize(result_path: str = "samples.jsonl") -> Optional[str]:
+    """
+    在命令行中自动执行 evalplus.sanitize --samples result_path
+    返回result_path前缀加上"-sanitized.jsonl"
+    """
+    command = ["evalplus.sanitize", "--samples", result_path]
+
+    subprocess.run(command, check=True)
+
+    base_name = os.path.splitext(result_path)[0]
+    sanitized_path = f"{base_name}-sanitized.jsonl"
+
+    return sanitized_path
+
+
+@handle_exception(
+    exception_type=subprocess.CalledProcessError,
+    exception_msg="Error in automatic_evalplus function",
+    default_return=False,
+)
+def automatic_evalplus(result_path: str = "samples.jsonl") -> bool:
+    """
+    在命令行中自动执行 evalplus.evaluate --dataset humaneval --samples samples.jsonl --parallel 2 --base-only
+    """
+    command = [
+        sys.executable,  # 使用当前 Python 解释器
+        "-m",
+        "evalplus.evaluate",
+        "--dataset",
+        "humaneval",
+        "--samples",
+        result_path,
+        "--parallel",
+        "2",
+        "--base-only",
+    ]
+
+    result = subprocess.run(command, check=True, capture_output=True, text=True)
+    logger.info(f"ouptput: \n {result.stdout}")
+    return True
+
+
+def extract_failure_tests(file_path: str = "samples_eval_results.json"):
+    task_results = read_json_file(file_path)
+
+    failed_tests = []
+    for task in task_results["eval"].values():
+        if task[0]["base_status"] == "fail":
+            failed_test = {
+                "task_id": task[0]["task_id"],
+            }
+            failed_tests.append(failed_test)
+    logger.info(f"length of failed tests: {len(failed_tests)}")
+
+    return failed_tests

examples/ags/w_action_node/graph.py

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+# -*- coding: utf-8 -*-
+# @Date    : 6/27/2024 22:07 PM
+# @Author  : didi
+# @Desc    : graph & an instance - humanevalgraph
+
+from typing import List
+
+from evalplus.data import get_human_eval_plus
+
+from examples.ags.w_action_node.operator import (
+    FuEnsemble,
+    Generate,
+    GenerateCode,
+    GenerateCodeBlock,
+    MdEnsemble,
+    Rephrase,
+    Review,
+    Revise,
+    Test,
+)
+from examples.ags.w_action_node.utils import extract_test_cases_from_jsonl
+from metagpt.llm import LLM
+
+
+class Graph:
+    def __init__(self, name: str, llm: LLM) -> None:
+        self.name = name
+        self.model = llm
+
+    def __call__():
+        NotImplementedError("Subclasses must implement __call__ method")
+
+    def optimize(dataset: List):
+        pass
+
+
+class HumanEvalGraph(Graph):
+    def __init__(self, name: str, llm: LLM, criteria: str, vote_count: int = 5) -> None:
+        super().__init__(name, llm)
+        self.criteria = criteria  # TODO 自动构建图时，图的初始参数与图所使用的算子要求的外部参数相匹配
+        self.generate_code = GenerateCode(llm=llm)
+        self.generate_code_block = GenerateCodeBlock(llm=llm)
+        self.review = Review(llm=llm, criteria=criteria)
+        self.revise = Revise(llm=llm)
+        self.rephrase = Rephrase(llm=llm)
+        self.tester = Test(llm=llm)
+        self.fuensemble = FuEnsemble(llm=llm)
+        self.mdensemble = MdEnsemble(llm=llm, vote_count=vote_count)
+
+    async def __call__(self, problem: str, ensemble_count: int = 3):
+        solution_list = []
+        for _ in range(ensemble_count):
+            solution = await self.generate_code_block(problem)
+            solution = solution.get("code_solution")
+            solution_list.append(solution)
+        solution = await self.mdensemble("code", solution_list, problem)
+        return solution
+
+    async def alpha_codium(self, problem_id: str, problem: str, ensemble_count: int = 3):
+        """
+        Paper: Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
+        Link: https://arxiv.org/abs/2404.14963
+        Flow: An incomplete version of alpha codium, implementing the basic process of rephrase -> code ensemble -> tes
+        """
+        test_cases = extract_test_cases_from_jsonl(problem_id)
+        entry_point = get_human_eval_plus()[problem_id]["entry_point"]
+        rephrase_problem = await self.rephrase(problem)  # 在rephrase 中拼接原始的问题描述
+        solution_list = []
+        for _ in range(ensemble_count):
+            solution = await self.generate_code_block.rephrase_generate(
+                problem, rephrase_problem, function_name=entry_point
+            )
+            solution = solution.get("code_solution")
+            solution_list.append(solution)
+        solution = await self.mdensemble("code", solution_list, problem)
+        solution = await self.tester(problem_id, problem, rephrase_problem, solution, test_cases)
+        return solution
+
+    async def review_revise_ensemble(self, problem: str, ensemble_count: int = 2, revise_round: int = 3):
+        solution_list = []
+        for _ in range(ensemble_count):
+            solution = await self.single_solve(problem, revise_round)
+            solution_list.append(solution)
+        solution = await self.ensemble(solution_list, problem)
+        return solution
+
+    async def simple_ensemble(self, problem: str, ensemble_count: int = 3):
+        solution_list = []
+        for _ in range(ensemble_count):
+            solution = await self.generate_code(problem)
+            # solution = await self.generate_code_block(problem)
+            solution = solution.get("code_solution")
+            solution_list.append(solution)
+        solution = await self.fuensemble(solution_list, problem)
+        return solution
+
+    async def single_solve(self, problem: str, max_loop: int):
+        solution = await self.generate_code(problem)
+        solution = solution.get("code_solution")
+        for _ in range(max_loop):
+            review_feedback = await self.review(problem, solution)
+            if review_feedback["review_result"]:
+                break
+            solution = await self.revise(problem, solution, review_feedback["feedback"])
+            solution = solution.get("revised_solution")
+        return solution
+
+
+class Gsm8kGraph(Graph):
+    def __init__(self, name: str, llm: LLM) -> None:
+        super().__init__(name, llm)
+        self.generate = Generate(llm=llm)
+        self.rephrase = Rephrase(llm=llm)
+
+    async def __call__(self, problem: str):
+        solution = self.generate(problem)
+        return solution
+
+
+class HotpotQAGraph(Graph):
+    def __init__(self, name: str, llm: LLM) -> None:
+        super().__init__(name, llm)
+        self.generate = Generate(llm=llm)
+        self.rephrase = Rephrase(llm=llm)
+
+    async def __call__(self, problem: str):
+        solution = self.generate(problem)
+        return solution