diff --git a/applications/lats/README.md b/applications/lats/README.md new file mode 100644 index 0000000..613ae9a --- /dev/null +++ b/applications/lats/README.md @@ -0,0 +1,106 @@ +# LATS (Language Agent Tree Search) – HumanEval on MASFactory + +This directory is a [MASFactory](https://github.com/BUPT-GAMMA/MASFactory) application that reproduces **LATS** (Language Agent Tree Search) on the **HumanEval** (programming) benchmark. + +- **Paper**: [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://arxiv.org/abs/2310.04406) (ICML 2024) +- **Upstream reference**: [LanguageAgentTreeSearch](https://github.com/andyz245/LanguageAgentTreeSearch) (programming / HumanEval) + +## Layout + +``` +applications/lats/ +├── main.py # Entry: argparse, load dataset, build graph, run loop, tee to log +├── README.md +├── assets/ +│ └── config/ # Config (default dataset path, etc.); datasets not in repo +│ └── defaults.json +├── workflows/ # Graph and controller +│ ├── graph.py # RootGraph, LATSTemplate (Loop with LLM_Agent, Executor, Reflection), run_one_problem +│ └── controller.py # lats_controller_logic (MCTS select / expand / backprop / terminate) +├── components/ +│ ├── agents.py # run_humaneval_forward, set_print_code_attempts (no custom Agent/CustomNode classes) +│ └── tree.py # LATSNode, TreeManager, gather_context_from_tree +├── humaneval/ # HumanEval data and execution +│ ├── load.py # load_humaneval_jsonl, parse_internal_tests_from_test, extract_python_code +│ ├── executor.py # run_internal_tests, full_evaluate, verify_evaluation +│ └── timeout_utils.py # function_with_timeout +└── utils/ + └── tee.py # Tee output to terminal and optional log file +``` + +## Design + +- **No custom Agent or CustomNode subclasses.** The graph uses only MASFactory’s built-in **Agent** and **CustomNode** via **NodeTemplate**: + - **LLM_Agent**: `NodeTemplate(Agent, model=..., instructions=..., formatters=[ParagraphMessageFormatter(), TwinsFieldTextFormatter()])` + - **Executor**: `NodeTemplate(CustomNode, forward=run_humaneval_forward, pull_keys=..., push_keys=...)` + - **Reflection**: `NodeTemplate(Agent, model=..., instructions=..., formatters=[...])` +- **Messaging** is done via **edges** and Loop **pull_keys** (no Agent/MessageFormatter overloading for pass-through). Controller sends `reflexion_prompt` to LLM; controller sends `problem` and `internal_tests` to Executor; LLM sends `content` to Executor; Executor sends `action`/`observation` to Reflection and `action`/`observation`/`reward`/`full_passed` to controller; Reflection sends only `reflection` to controller. +- **Executor** and **Reflection** are separate nodes (executor logic in `run_humaneval_forward`; reflection is a plain Agent). NodeTemplates are wrapped in **Shared(...)** so the Loop config can be cloned without deepcopying them. + +## Context and memory in this port + +In the LATS paper, **context** and **memory** are conceptual. In this app they are not separate nodes: + +- **Context**: Built inside the controller and sent to the LLM as `reflexion_prompt` (previous attempts, test results, reflections). See `workflows/controller.py` and `gather_context_from_tree` in `components/tree.py`. +- **Memory**: The search tree (`LATSNode`, `TreeManager` in `components/tree.py`) held by the controller; it is updated each loop (selection, backprop, new children). + +## Setup + +From the MASFactory repo root: + +```bash +pip install masfactory openai +``` + +Optional: set default dataset in `assets/config/defaults.json` (`"dataset_path": "path/to/HumanEval.jsonl.gz"`). + +Environment variables: + +| Variable | Description | +|----------|-------------| +| **OPENAI_API_KEY** | Required | +| **OPENAI_API_BASE** | Optional (proxy/custom endpoint) | +| **LATS_MODEL** | Optional, default `gpt-4` | +| **LATS_MAX_ITERS** | Optional, default `8` | +| **NUMBER_OF_TESTS** | Optional, default `2` | +| **MASFACTORY_VISUALIZER_PORT** | Optional, for runtime view | + +Example (PowerShell): + +```powershell +$env:OPENAI_API_KEY="your-key" +$env:OPENAI_API_BASE="https://your-endpoint/v1/" +$env:LATS_MODEL="你的模型" +... +``` + +## Run + +From the **MASFactory repo root**: + +```bash +python applications/lats/main.py --dataset "path/to/HumanEval.jsonl.gz" --log logs/lats.log +``` + +Or from **applications/lats**: + +```bash +python main.py --dataset "path/to/HumanEval.jsonl.gz" --log logs/lats.log +``` + +Examples: + +```bash +# Limit to 5 problems +python applications/lats/main.py --dataset "path/to/HumanEval.jsonl.gz" --limit 5 --log logs/lats.log + +# Paper-aligned: max_iters=8, number_of_tests=2 (defaults) +python applications/lats/main.py --dataset "path/to/HumanEval.jsonl.gz" --log logs/lats.log +``` + +Output is printed to the terminal and, when `--log` is set, appended to the given file. + +## Metrics + +- **Pass@1**: fraction of problems for which the best solution passes the full HumanEval test. +- Defaults match the upstream GPT-4 setup: `max_iters=8`, `number_of_tests=2`. diff --git a/applications/lats/__init__.py b/applications/lats/__init__.py new file mode 100644 index 0000000..aeb3319 --- /dev/null +++ b/applications/lats/__init__.py @@ -0,0 +1 @@ +# LATS application (HumanEval on MASFactory) diff --git a/applications/lats/assets/config/.gitkeep b/applications/lats/assets/config/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/applications/lats/assets/config/defaults.json b/applications/lats/assets/config/defaults.json new file mode 100644 index 0000000..46ead8c --- /dev/null +++ b/applications/lats/assets/config/defaults.json @@ -0,0 +1,5 @@ +{ + "dataset_path": "", + "max_iters": 8, + "number_of_tests": 2 +} diff --git a/applications/lats/components/__init__.py b/applications/lats/components/__init__.py new file mode 100644 index 0000000..e813804 --- /dev/null +++ b/applications/lats/components/__init__.py @@ -0,0 +1 @@ +# LATS custom components (formatters, agents, tree) diff --git a/applications/lats/components/agents.py b/applications/lats/components/agents.py new file mode 100644 index 0000000..f2ea007 --- /dev/null +++ b/applications/lats/components/agents.py @@ -0,0 +1,102 @@ +""" +Helper utilities for the LATS HumanEval executor. + +This module provides only: +- set_print_code_attempts (for --print-code) +- run_humaneval_forward (used by CustomNode in the workflow) + +No custom Agent or CustomNode subclasses; the graph uses system Agent and CustomNode via NodeTemplate. +""" + +from ..humaneval.load import extract_python_code, parse_internal_tests_from_test +from ..humaneval.executor import run_internal_tests, full_evaluate +from ..utils.tee import tee, get_log_file + +_print_code_attempts = False + + +def set_print_code_attempts(value: bool) -> None: + """Enable or disable printing each generated code attempt.""" + global _print_code_attempts + _print_code_attempts = value + + +def _print_generated_func_body(func_body: str, problem_name: str = "") -> None: + """Print generated code to terminal and log (if --log and --print-code).""" + if not _print_code_attempts: + return + title = "GENERATED FUNC BODY" + if problem_name: + title += f" [{problem_name}]" + tee(f"\n--------------------- {title} ---------------------", get_log_file()) + tee(func_body, get_log_file()) + tee("------------------------------------------\n", get_log_file()) + + +def run_humaneval_forward(input_dict: dict, attrs: dict | None = None) -> dict: + """HumanEval execution (originally HumanEvalEnvironment._forward). + + Accept both edge-passed fields (message) and attribute-based fields (attrs), + and prefer explicit message values when present. + """ + attrs = attrs or {} + content = ( + input_dict.get("action", "") + or input_dict.get("content", "") + or (attrs.get("content", "") if isinstance(attrs, dict) else "") + ) + raw = str(content).strip() + + problem = input_dict.get("problem") + if not isinstance(problem, dict): + candidate = attrs.get("problem") if isinstance(attrs, dict) else None + problem = candidate if isinstance(candidate, dict) else {} + + internal_tests = input_dict.get("internal_tests") + if not isinstance(internal_tests, list): + candidate_tests = attrs.get("internal_tests") if isinstance(attrs, dict) else None + internal_tests = candidate_tests if isinstance(candidate_tests, list) else [] + entry_point = problem.get("entry_point", "") + test = problem.get("test", "") + prompt = problem.get("prompt", "") + + fail_safe = { + "observation": "Error: No valid Python code.", + "reward": 0.0, + "reward_internal": 0.0, + "reward_real": 0.0, + "full_passed": False, + "action": raw, + "problem": problem, + "internal_tests": internal_tests, + } + + code = extract_python_code(raw) + if not code: + fail_safe["observation"] = "Error: Use a ```python ... ``` block or full function." + return fail_safe + if "def " not in code and prompt: + code = prompt.rstrip() + "\n" + code + + if _print_code_attempts: + _print_generated_func_body(code, problem.get("name", "")) + + if not internal_tests: + internal_tests = parse_internal_tests_from_test(test, max_tests=6) + + is_passing_internal, feedback, reward_internal = run_internal_tests( + code, internal_tests, timeout=5 + ) + reward_real = 1.0 if full_evaluate(entry_point, code, test, timeout=10) else 0.0 + reward = reward_internal + reward_real + + return { + "observation": feedback, + "reward": reward, + "reward_internal": reward_internal, + "reward_real": reward_real, + "full_passed": reward_real >= 1.0, + "action": code, + "problem": problem, + "internal_tests": internal_tests, + } diff --git a/applications/lats/components/tree.py b/applications/lats/components/tree.py new file mode 100644 index 0000000..7c3afe4 --- /dev/null +++ b/applications/lats/components/tree.py @@ -0,0 +1,84 @@ +""" +LATS search tree and MCTS: node, selection, backprop, context gathering. +""" +import math +from typing import List, Tuple + + +class LATSNode: + def __init__( + self, + solution: str = "", + parent: "LATSNode | None" = None, + context: str = "", + depth: int = 0, + ): + self.solution = solution + self.parent = parent + self.children: List[LATSNode] = [] + self.value = 0.0 + self.visits = 0 + self.context = context + self.depth = depth + self.reflection = "" + self.test_feedback = "" + + def uct(self, exploration_weight: float = 1.0) -> float: + if self.visits == 0: + return self.value + p = self.parent + p_visits = p.visits if p else 1 + return (self.value / self.visits) + exploration_weight * math.sqrt( + math.log(max(1, p_visits)) / self.visits + ) + + def best_child(self) -> "LATSNode | None": + if not self.children: + return None + return max(self.children, key=lambda c: c.uct()) + + def best_child_value(self) -> "LATSNode | None": + if not self.children: + return None + return max(self.children, key=lambda c: c.value) + + def update(self, reward: float): + self.visits += 1 + self.value += reward + + +def gather_context_from_tree( + node: LATSNode, +) -> Tuple[List[str], List[str], List[str]]: + """Collect (solution, test_feedback, reflection) from current node to root for reflexion context.""" + impls, feedbacks, reflections = [], [], [] + while node: + if node.solution: + impls.append(node.solution) + feedbacks.append(node.test_feedback or "") + reflections.append(node.reflection or "") + node = node.parent + return impls[::-1], feedbacks[::-1], reflections[::-1] + + +class TreeManager: + def __init__(self, problem: dict, root: LATSNode): + self.problem = problem + self.root = root + self.current_node = root + self._max_iters = 8 + + def selection(self) -> LATSNode: + node = self.root + while node.children: + child = node.best_child() + if child is None: + break + node = child + return node + + def backprop(self, node: LATSNode, reward: float): + temp = node + while temp: + temp.update(reward) + temp = temp.parent diff --git a/applications/lats/humaneval/__init__.py b/applications/lats/humaneval/__init__.py new file mode 100644 index 0000000..ba7feca --- /dev/null +++ b/applications/lats/humaneval/__init__.py @@ -0,0 +1 @@ +# HumanEval data loading and execution diff --git a/applications/lats/humaneval/executor.py b/applications/lats/humaneval/executor.py new file mode 100644 index 0000000..8e78e6b --- /dev/null +++ b/applications/lats/humaneval/executor.py @@ -0,0 +1,82 @@ +""" +HumanEval executor: internal tests + full evaluate. Uses fresh globals per run to avoid cross-problem pollution. +""" +import re +from typing import List, Tuple + +from .timeout_utils import function_with_timeout + + +def _fresh_globals(): + """Fresh namespace for each evaluation to avoid function name pollution between problems.""" + import builtins + g = {"__builtins__": builtins} + exec("from typing import *", g) + return g + + +def run_internal_tests( + func: str, tests: List[str], timeout: int = 5 +) -> Tuple[bool, str, float]: + """Run internal tests; return (all_passed, feedback_string, reward_internal = passed/total).""" + success_tests = [] + failed_tests = [] + for test in tests: + g = _fresh_globals() + try: + function_with_timeout(exec, (f"{func}\n{test}", g), timeout) + success_tests.append(test) + except Exception: + out = get_test_output(func, test, timeout) + failed_tests.append(f"{test} # output: {out}") + feedback = "Tested passed:" + for t in success_tests: + feedback += f"\n{t}" + feedback += "\n\nTests failed:" + for t in failed_tests: + feedback += f"\n{t}" + n = len(tests) + reward_internal = (len(success_tests) / n) if n else 0.0 + return (len(failed_tests) == 0, feedback, reward_internal) + + +def get_test_output(func: str, assert_statement: str, timeout: int) -> str: + """Execute single assert and return actual output (for failure message).""" + g = _fresh_globals() + try: + exec(func, g) + s = re.sub(r"^assert\s+", "", assert_statement.strip()).split(" # ")[0].strip() + if " == " in s: + call_str = s.split(" == ")[0].strip() + else: + call_str = s + return str(function_with_timeout(eval, (call_str, g), timeout)) + except TimeoutError: + return "TIMEOUT" + except Exception as e: + return str(e) + + +def full_evaluate(entry_point: str, func: str, test: str, timeout: int = 10) -> bool: + """Official HumanEval full evaluation: func + test + check(entry_point). Uses fresh namespace.""" + code = f"{func}\n\n{test}\n\ncheck({entry_point})" + g = _fresh_globals() + try: + function_with_timeout(exec, (code, g), timeout) + return True + except Exception: + return False + + +def verify_evaluation(problem: dict) -> None: + """Startup check: wrong implementation must be rejected, else evaluation logic is buggy.""" + import sys + entry_point = problem.get("entry_point", "") + test = problem.get("test", "") + if not entry_point or not test: + return + wrong_impl = f"def {entry_point}(*args, **kwargs):\n return 0" + if full_evaluate(entry_point, wrong_impl, test, timeout=5): + print("ERROR: Evaluation bug: a wrong solution was marked as PASSED. Fix full_evaluate.") + sys.exit(1) + print("Evaluation verification passed (wrong solution correctly rejected).") diff --git a/applications/lats/humaneval/load.py b/applications/lats/humaneval/load.py new file mode 100644 index 0000000..9643a90 --- /dev/null +++ b/applications/lats/humaneval/load.py @@ -0,0 +1,53 @@ +""" +Load HumanEval dataset (.jsonl / .jsonl.gz) and parse internal tests from test string. +""" +import json +import gzip +import random +import re +from typing import List + + +def load_humaneval_jsonl(path: str) -> List[dict]: + """Load HumanEval items from .jsonl or .jsonl.gz.""" + items = [] + if path.endswith(".gz"): + with gzip.open(path, "rt", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + items.append(json.loads(line)) + else: + with open(path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line: + continue + items.append(json.loads(line)) + return items + + +def parse_internal_tests_from_test(test_str: str, max_tests: int = 6) -> List[str]: + """Parse assert lines from HumanEval test string as internal tests (aligned with LATS number_of_tests).""" + asserts = [] + for line in test_str.splitlines(): + line = line.strip() + if line.startswith("assert candidate(") or (line.startswith("assert ") and "candidate(" in line): + asserts.append(line) + if not asserts: + return asserts + if len(asserts) > max_tests: + asserts = random.sample(asserts, max_tests) + return asserts + + +def extract_python_code(raw: str) -> str: + """Extract Python code block or first complete function from LLM output.""" + raw = (raw or "").strip() + code_match = re.search(r"```python\s*(.*?)\s*```", raw, re.DOTALL) + if code_match: + return code_match.group(1).strip() + if "def " in raw: + return raw + return "" diff --git a/applications/lats/humaneval/timeout_utils.py b/applications/lats/humaneval/timeout_utils.py new file mode 100644 index 0000000..4d8e0a9 --- /dev/null +++ b/applications/lats/humaneval/timeout_utils.py @@ -0,0 +1,31 @@ +""" +Timeout wrapper for test execution (no dependency on LATS repo). +""" +from threading import Thread + + +class _PropagatingThread(Thread): + def run(self): + self.exc = None + try: + self.ret = self._target(*self._args, **self._kwargs) + except BaseException as e: + self.exc = e + + def join(self, timeout=None): + super().join(timeout) + if self.exc: + raise self.exc + return self.ret + + +def function_with_timeout(func, args, timeout): + r = [] + def w(): + r.append(func(*args)) + t = _PropagatingThread(target=w) + t.start() + t.join(timeout) + if t.is_alive(): + raise TimeoutError() + return r[0] diff --git a/applications/lats/main.py b/applications/lats/main.py new file mode 100644 index 0000000..58ef03a --- /dev/null +++ b/applications/lats/main.py @@ -0,0 +1,176 @@ +""" +LATS (Language Agent Tree Search) – HumanEval on MASFactory. +Standard entry point: parse args, load dataset, build graph, run problems, tee to log. +Reference: https://arxiv.org/abs/2310.04406 and LanguageAgentTreeSearch-main/programming. +""" +import os +import sys +import json +import argparse + +# Ensure repo root (parent of lats/) is on path so "lats" package resolves when run from lats/ or repo root +_APP_ROOT = os.path.dirname(os.path.abspath(__file__)) +_REPO_ROOT = os.path.dirname(_APP_ROOT) +if _REPO_ROOT not in sys.path: + sys.path.insert(0, _REPO_ROOT) + +from lats.humaneval.load import load_humaneval_jsonl, parse_internal_tests_from_test +from lats.humaneval.executor import verify_evaluation +from lats.workflows.graph import ( + build_graph, + run_one_problem, + LATS_MAX_ITERS, + NUMBER_OF_TESTS, +) +from lats.utils.tee import tee, set_log_file, get_log_file +from lats.components.agents import set_print_code_attempts + + +def _default_dataset_path() -> str: + p = os.path.join(_APP_ROOT, "assets", "config", "defaults.json") + if os.path.isfile(p): + try: + with open(p, "r", encoding="utf-8") as f: + d = json.load(f) + if d.get("dataset_path"): + return d["dataset_path"] + except Exception: + pass + return "" + + +def main(): + parser = argparse.ArgumentParser( + description="LATS (Language Agent Tree Search) on HumanEval via MASFactory." + ) + parser.add_argument( + "--dataset", + default="", + help="HumanEval path: .jsonl or .jsonl.gz", + ) + parser.add_argument("--max_iters", type=int, default=LATS_MAX_ITERS) + parser.add_argument("--number_of_tests", type=int, default=NUMBER_OF_TESTS) + parser.add_argument("--limit", type=int, default=0, help="Limit number of problems (0 = all)") + parser.add_argument( + "--print-code", + action="store_true", + help="Print each generated attempt (default: final solution only)", + ) + parser.add_argument( + "--log", + type=str, + default="", + help="Append same output to file (e.g. logs/lats.log)", + ) + args = parser.parse_args() + + set_print_code_attempts(args.print_code) + log_file = None + if args.log: + try: + os.makedirs(os.path.dirname(args.log) or ".", exist_ok=True) + log_file = open(args.log, "a", encoding="utf-8") + set_log_file(log_file) + except Exception as e: + print(f"Warning: could not open log file {args.log}: {e}", flush=True) + + max_iters = args.max_iters + number_of_tests = args.number_of_tests + dataset_path = args.dataset or _default_dataset_path() + if not dataset_path: + dataset_path = os.path.join( + os.path.dirname(_APP_ROOT), + "1", + "LanguageAgentTreeSearch-main", + "programming", + "benchmarks", + "humaneval-py.jsonl", + ) + + dataset = load_humaneval_jsonl(dataset_path) + if args.limit > 0: + dataset = dataset[: args.limit] + + tee(f"Dataset: {dataset_path}", log_file) + tee(f"Loaded {len(dataset)} problems. max_iters={max_iters}, number_of_tests={number_of_tests}", log_file) + model_name = os.environ.get("LATS_MODEL", "gpt-4") + tee(f"Model: {model_name}", log_file) + + if dataset: + verify_evaluation({ + "entry_point": dataset[0].get("entry_point", ""), + "test": dataset[0].get("test", ""), + "prompt": dataset[0].get("prompt", ""), + }) + + g = build_graph() + + _vis_port = os.environ.get("MASFACTORY_VISUALIZER_PORT", "") + _vis_host = os.environ.get("MASFACTORY_VISUALIZER_HOST", "127.0.0.1") + try: + import masfactory.visualizer as _vis + _connected_bridge = _vis.connect_bridge(timeout_s=5.0) + if _connected_bridge is not None: + _connected_bridge.attach_graph(g) + _vis.get_bridge = lambda: _connected_bridge + print("Visualizer connected: runtime view enabled.") + elif _vis_port: + print("Visualizer: connection failed (runtime view disabled).") + print(f" Tried {_vis_host}:{_vis_port} — ensure MASFactory extension is open and listening on this port.") + except Exception as e: + if _vis_port: + print("Visualizer connect error:", e) + + api_key = os.environ.get("OPENAI_API_KEY", "").strip() + if not api_key: + print("Error: OPENAI_API_KEY is not set. Set it first, e.g.:") + print(" set OPENAI_API_KEY=sk-your-key (Windows)") + print(" export OPENAI_API_KEY=sk-your-key (Linux/Mac)") + print("Get a key: https://platform.openai.com/account/api-keys") + sys.exit(1) + print("Using OPENAI_API_KEY from env. (401 = invalid key; check/regenerate at https://platform.openai.com/account/api-keys)") + if not _vis_port: + print("Tip: set MASFACTORY_VISUALIZER_PORT to enable the visualizer runtime view.") + + num_success = 0 + for idx, item in enumerate(dataset): + problem = { + "name": item.get("name", item.get("task_id", "")), + "prompt": item.get("prompt", ""), + "entry_point": item.get("entry_point", ""), + "test": item.get("test", ""), + } + internal_tests = parse_internal_tests_from_test( + problem["test"], max_tests=number_of_tests + ) + try: + best_code, passed = run_one_problem( + problem, g, internal_tests, max_iters, number_of_tests + ) + except Exception as e: + best_code, passed = "", False + err_msg = str(e).split("\n")[0][:80] + tee(f"Warning: problem {idx+1} failed ({err_msg}), treating as not passed.", log_file) + if passed: + num_success += 1 + acc = round(num_success / (idx + 1), 2) + tee(f"completed {idx+1}/{len(dataset)}: acc = {acc}", log_file) + pname = problem.get("name", item.get("task_id", f"problem_{idx+1}")) + tee(f"\n--------------------- FINAL SOLUTION [{pname}] passed={bool(passed)} ---------------------", log_file) + tee(best_code if best_code else "(none)", log_file) + tee("------------------------------------------\n", log_file) + item["solution"] = best_code + item["is_solved"] = passed + item["acc"] = acc + + tee(f"Done. Pass@1 acc = {num_success}/{len(dataset)} = {round(num_success/len(dataset), 2)}", log_file) + if log_file is not None: + try: + log_file.close() + except Exception: + pass + set_log_file(None) + + +if __name__ == "__main__": + main() diff --git a/applications/lats/utils/__init__.py b/applications/lats/utils/__init__.py new file mode 100644 index 0000000..35cefe0 --- /dev/null +++ b/applications/lats/utils/__init__.py @@ -0,0 +1 @@ +# LATS application utilities diff --git a/applications/lats/utils/tee.py b/applications/lats/utils/tee.py new file mode 100644 index 0000000..fc64aab --- /dev/null +++ b/applications/lats/utils/tee.py @@ -0,0 +1,29 @@ +""" +Tee output to terminal and optional log file. +Used by main to duplicate progress/solutions to --log file. +""" +import sys +from typing import Optional, TextIO + +_lats_log_file: Optional[TextIO] = None + + +def set_log_file(f: Optional[TextIO]) -> None: + global _lats_log_file + _lats_log_file = f + + +def get_log_file() -> Optional[TextIO]: + return _lats_log_file + + +def tee(s: str, log_file: Optional[TextIO] = None) -> None: + """Print to stdout and append to log_file (or global _lats_log_file) if set.""" + print(s, flush=True) + f = log_file if log_file is not None else _lats_log_file + if f is not None: + try: + f.write(s.rstrip() + "\n") + f.flush() + except Exception: + pass diff --git a/applications/lats/workflows/__init__.py b/applications/lats/workflows/__init__.py new file mode 100644 index 0000000..0505e1d --- /dev/null +++ b/applications/lats/workflows/__init__.py @@ -0,0 +1 @@ +# LATS workflow (graph + controller) diff --git a/applications/lats/workflows/controller.py b/applications/lats/workflows/controller.py new file mode 100644 index 0000000..430bb30 --- /dev/null +++ b/applications/lats/workflows/controller.py @@ -0,0 +1,103 @@ +""" +LATS controller: MCTS selection, expand, backprop, terminate. Uses tree set by graph before each invoke. +""" +from typing import Any, Optional + +from ..components.tree import LATSNode, TreeManager, gather_context_from_tree + +_lats_tree: Optional[TreeManager] = None + + +def set_lats_tree(tm: TreeManager | None) -> None: + global _lats_tree + _lats_tree = tm + + +def lats_controller_logic(message: dict, _attrs: Any) -> bool: + """Return True to terminate loop; otherwise set reflexion_prompt and return False.""" + global _lats_tree + if _lats_tree is None: + message["final_code"] = "" + message["final_passed"] = False + return True + + action = message.get("action") or message.get("(not set yet)") + if action == "(not set yet)": + action = "" + observation = message.get("observation", "") + reward = message.get("reward", 0.0) + try: + reward = float(reward) if str(reward) != "(not set yet)" else 0.0 + except Exception: + reward = 0.0 + full_passed = message.get("full_passed", False) + if isinstance(full_passed, str) and "(not set yet)" in str(full_passed): + full_passed = False + full_passed = bool(full_passed) + reflection = message.get("reflection", "") + + root = _lats_tree.root + # First round: init root from first LLM output; else add child and backprop + if action and action not in ("(not set yet)", "Empty", "Invalid_Instruction"): + if not root.solution and root.visits == 0: + root.solution = action + root.test_feedback = observation + root.reflection = reflection + root.visits = 1 + root.value = reward + else: + selected = _lats_tree.current_node + child = LATSNode( + solution=action, parent=selected, depth=selected.depth + 1 + ) + child.test_feedback = observation + child.reflection = reflection + selected.children.append(child) + _lats_tree.backprop(child, reward) + + if full_passed or root.visits >= _lats_tree._max_iters: + best_node = root.best_child_value() if root.children else root + best_code = (best_node.solution if best_node else "") or action or "" + if full_passed and action: + best_code = action + message["final_code"] = best_code + message["final_passed"] = full_passed + return True + + selected = _lats_tree.selection() + _lats_tree.current_node = selected + path_impls, path_feedbacks, path_reflections = gather_context_from_tree( + selected + ) + + # Build reflexion prompt (aligned with generator_utils.generate_with_accumulated_context) + if not path_impls: + reflexion_prompt = ( + "You are an AI that only responds with Python code. Write your full implementation (restate the function signature). " + "Use a Python code block: ```python ... ```\n\n" + + (_lats_tree.problem.get("prompt") or "") + ) + else: + parts = [] + for i, (impl, fb, ref) in enumerate( + zip(path_impls, path_feedbacks, path_reflections) + ): + impl_short = impl[:2000] + "..." if len(impl) > 2000 else impl + parts.append( + f"[previous impl {i+1}]:\n```python\n{impl_short}\n```\n" + f"[unit test results {i+1}]:\n{fb}\n[reflection {i+1}]:\n{ref}" + ) + reflexion_prompt = ( + "You are an AI Python assistant. You will be given previous implementation(s), unit test results, and self-reflections. " + "Write your full improved implementation (restate the function signature). Use only a ```python ... ``` block.\n\n" + + "\n\n".join(parts) + + "\n\n[improved impl]:\n" + + (_lats_tree.problem.get("prompt") or "") + ) + + message["reflexion_prompt"] = reflexion_prompt + message["action"] = "(not set yet)" + message["observation"] = "(not set yet)" + message["reward"] = 0.0 + message["reflection"] = "" + return False diff --git a/applications/lats/workflows/graph.py b/applications/lats/workflows/graph.py new file mode 100644 index 0000000..3db503d --- /dev/null +++ b/applications/lats/workflows/graph.py @@ -0,0 +1,207 @@ +""" +LATS workflow: build RootGraph + Loop (LLM -> Executor -> Reflection -> controller), run_one_problem. +Uses only system Agent and CustomNode via NodeTemplate (no custom agent classes). +""" +import os +from typing import List, Tuple + +from masfactory import RootGraph, NodeTemplate, Loop, Agent, CustomNode, Shared +from masfactory.core.message import ParagraphMessageFormatter, TwinsFieldTextFormatter +from masfactory import OpenAIModel + +from ..components.agents import run_humaneval_forward +from ..components.tree import LATSNode, TreeManager +from .controller import lats_controller_logic, set_lats_tree +from ..humaneval.executor import full_evaluate + +# Paper/source run_lats_gpt4.sh: max_iters=8, number_of_tests=2 +LATS_MAX_ITERS = int(os.environ.get("LATS_MAX_ITERS", "8")) +NUMBER_OF_TESTS = int(os.environ.get("NUMBER_OF_TESTS", "2")) + +EXECUTOR_PUSH_KEYS = { + "observation": "observation", + "reward": "reward", + "action": "action", + "full_passed": "full_passed", +} + +model_instance = OpenAIModel( + api_key=os.environ.get("OPENAI_API_KEY", ""), + base_url=os.environ.get("OPENAI_API_BASE", ""), + model_name=os.environ.get("LATS_MODEL", "gpt-4"), +) + +LLMAgentTemplate = NodeTemplate( + Agent, + model=model_instance, + instructions=( + "You output ONLY Python code in a ```python ... ``` block. " + "No explanations. Restate the function signature in your implementation." + ), + prompt_template="{reflexion_prompt}", + formatters=[ + ParagraphMessageFormatter(), + TwinsFieldTextFormatter(), + ], +) + +ExecutorTemplate = NodeTemplate( + CustomNode, + forward=run_humaneval_forward, + pull_keys={ + "problem": "problem", + "internal_tests": "internal_tests", + "content": "content", + }, + push_keys=dict(EXECUTOR_PUSH_KEYS), +) + +ReflectionTemplate = NodeTemplate( + Agent, + model=model_instance, + instructions=( + "You are a Python programming assistant. Given a function implementation " + "and unit test results, write a few sentences explaining why the " + "implementation is wrong. Do NOT output code, only the explanation." + ), + prompt_template=( + "[function impl]:\n```python\n{action}\n```\n\n" + "[unit test results]:\n{observation}\n\n[self-reflection]:" + ), + pull_keys={ + "action": "Candidate implementation under review.", + "observation": "Unit-test feedback for the candidate implementation.", + }, + formatters=[ + ParagraphMessageFormatter(), + TwinsFieldTextFormatter(), + ], +) + +# Shared() prevents NodeTemplate from being deepcopied when the Loop config is cloned (avoids RLock/pickle errors). +loop_nodes = [ + ("LLM_Agent", Shared(LLMAgentTemplate)), + ("Executor", Shared(ExecutorTemplate)), + ("Reflection", Shared(ReflectionTemplate)), +] + +LATSTemplate = NodeTemplate( + Loop, + max_iterations=LATS_MAX_ITERS, + terminate_condition_function=lats_controller_logic, + nodes=loop_nodes, + edges=[ + # Controller provides the next prompt; problem/internal_tests in loop attributes. + ( + "controller", + "LLM_Agent", + {"reflexion_prompt": "reflexion_prompt"}, + ), + # Controller also sends problem/internal_tests to Executor (so Executor gets them without LLM pass-through). + ( + "controller", + "Executor", + {"problem": "problem", "internal_tests": "internal_tests"}, + ), + # LLM_Agent outputs candidate code only. + ( + "LLM_Agent", + "Executor", + {"content": "content"}, + ), + # Executor sends implementation and test output to Reflection. + ( + "Executor", + "Reflection", + {"action": "action", "observation": "observation"}, + ), + # Executor sends evaluation state to controller. + ( + "Executor", + "controller", + { + "action": "action", + "observation": "observation", + "reward": "reward", + "full_passed": "full_passed", + }, + ), + # Reflection sends only the explanation to controller. + ( + "Reflection", + "controller", + {"reflection": "reflection"}, + ), + ], + pull_keys={"problem": "problem", "internal_tests": "internal_tests"}, + push_keys={"final_code": "final_code", "final_passed": "final_passed"}, +) + + +def build_graph() -> RootGraph: + """Build LATS RootGraph with single LATS node (Loop).""" + g = RootGraph( + name="LATS_Runner", + nodes=[("LATS", LATSTemplate)], + edges=[ + ("entry", "LATS", {"problem": "problem", "internal_tests": "internal_tests"}), + ("LATS", "exit", {"final_code": "final_code", "final_passed": "final_passed"}), + ], + ) + g.build() + try: + lats_loop = getattr(g, "_nodes", {}).get("LATS") + if lats_loop is not None and hasattr(lats_loop, "_nodes"): + env_node = lats_loop._nodes.get("Executor") + if env_node is not None and hasattr(env_node, "set_push_keys"): + env_node.set_push_keys(dict(EXECUTOR_PUSH_KEYS)) + except Exception: + pass + return g + + +def run_one_problem( + problem: dict, + graph: RootGraph, + internal_tests: List[str], + max_iters: int, + number_of_tests: int, +) -> Tuple[str, bool]: + """Run LATS for one problem; return (best_solution, passed).""" + set_lats_tree(None) + prompt = problem.get("prompt", "") + simple_prompt = ( + "You output ONLY Python code in a ```python ... ``` block. No explanations. " + "Write your full implementation (restate the function signature).\n\n" + prompt + ) + root = LATSNode(solution="", context=prompt) + tm = TreeManager(problem, root) + tm._max_iters = max_iters + set_lats_tree(tm) + + initial_input = { + "problem": problem, + "internal_tests": internal_tests, + "reflexion_prompt": simple_prompt, + } + result, _ = graph.invoke(initial_input) + final_code = result.get("final_code", "") or "" + final_passed = result.get("final_passed", False) + if isinstance(final_passed, str) and "(not set yet)" in str(final_passed): + final_passed = False + final_passed = bool(final_passed) + if not final_code and tm and tm.root: + best_node = tm.root.best_child_value() if tm.root.children else tm.root + if best_node and getattr(best_node, "solution", None): + final_code = best_node.solution + elif getattr(tm.root, "solution", None): + final_code = tm.root.solution + if final_code and not final_passed: + final_passed = full_evaluate( + tm.problem.get("entry_point", ""), + final_code, + tm.problem.get("test", ""), + timeout=10, + ) + set_lats_tree(None) + return final_code, final_passed diff --git a/masfactory-visualizer/package-lock.json b/masfactory-visualizer/package-lock.json index b0dbd0f..0ca87b1 100644 --- a/masfactory-visualizer/package-lock.json +++ b/masfactory-visualizer/package-lock.json @@ -33,9 +33,9 @@ "license": "MIT" }, "node_modules/@eslint-community/eslint-utils": { - 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Please update. Support for old versions may be purchased (at exorbitant rates) by contacting i@izs.me", "dev": true, "license": "ISC", "dependencies": { @@ -1683,13 +1614,6 @@ "dev": true, "license": "ISC" }, - "node_modules/graphemer": { - "version": "1.4.0", - "resolved": "https://registry.npmjs.org/graphemer/-/graphemer-1.4.0.tgz", - "integrity": "sha512-EtKwoO6kxCL9WO5xipiHTZlSzBm7WLT627TqC/uVRd0HKmq8NXyebnNYxDoBi7wt8eTWrUrKXCOVaFq9x1kgag==", - "dev": true, - "license": "MIT" - }, "node_modules/has-flag": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz", @@ -1993,9 +1917,9 @@ } }, "node_modules/js-yaml": { - "version": "4.1.0", - "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.0.tgz", - "integrity": "sha512-wpxZs9NoxZaJESJGIZTyDEaYpl0FKSA+FB9aJiyemKhMwkxQg63h4T1KJgUGHpTqPDNRcmmYLugrRjJlBtWvRA==", + "version": "4.1.1", + "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz", + "integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==", "dev": true, "license": "MIT", "dependencies": { @@ -2136,30 +2060,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - 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"url": "https://www.patreon.com/feross" - }, - { - "type": "consulting", - "url": "https://feross.org/support" - } - ], - "license": "MIT" - }, "node_modules/randombytes": { "version": "2.1.0", "resolved": "https://registry.npmjs.org/randombytes/-/randombytes-2.1.0.tgz", @@ -2705,41 +2584,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/reusify": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/reusify/-/reusify-1.1.0.tgz", - "integrity": "sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==", - "dev": true, - "license": "MIT", - "engines": { - "iojs": ">=1.0.0", - "node": ">=0.10.0" - } - }, - "node_modules/run-parallel": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/run-parallel/-/run-parallel-1.2.0.tgz", - "integrity": "sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/feross" - }, - { - "type": "patreon", - "url": "https://www.patreon.com/feross" - }, - { - "type": "consulting", - "url": "https://feross.org/support" - } - ], - "license": "MIT", - "dependencies": { - "queue-microtask": "^1.2.2" - } - }, "node_modules/safe-buffer": { "version": "5.1.2", "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz", @@ -2748,9 +2592,9 @@ "license": "MIT" }, "node_modules/semver": { - "version": "7.7.3", - "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz", - "integrity": "sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==", + "version": "7.7.4", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.4.tgz", + "integrity": "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==", "dev": true, "license": "ISC", "bin": { @@ -3010,7 +2854,7 @@ "version": "7.2.3", "resolved": "https://registry.npmjs.org/glob/-/glob-7.2.3.tgz", "integrity": "sha512-nFR0zLpU2YCaRxwoCJvL6UvCH2JFyFVIvwTLsIf21AuHlMskA1hhTdk+LlYJtOlYt9v6dvszD2BGRqBL+iQK9Q==", - "deprecated": "Glob versions prior to v9 are no longer supported", + "deprecated": "Old versions of glob are not supported, and contain widely publicized security vulnerabilities, which have been fixed in the current version. Please update. Support for old versions may be purchased (at exorbitant rates) by contacting i@izs.me", "dev": true, "license": "ISC", "dependencies": { @@ -3041,6 +2885,54 @@ "node": "*" } }, + "node_modules/tinyglobby": { + "version": "0.2.15", + "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz", + "integrity": "sha512-j2Zq4NyQYG5XMST4cbs02Ak8iJUdxRM0XI5QyxXuZOzKOINmWurp3smXu3y5wDcJrptwpSjgXHzIQxR0omXljQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "fdir": "^6.5.0", + "picomatch": "^4.0.3" + }, + "engines": { + "node": ">=12.0.0" + }, + "funding": { + "url": "https://github.com/sponsors/SuperchupuDev" + } + }, + "node_modules/tinyglobby/node_modules/fdir": { + "version": "6.5.0", + "resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz", + "integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12.0.0" + }, + "peerDependencies": { + "picomatch": "^3 || ^4" + }, + "peerDependenciesMeta": { + "picomatch": { + "optional": true + } + } + }, + "node_modules/tinyglobby/node_modules/picomatch": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz", + "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/jonschlinkert" + } + }, "node_modules/to-regex-range": { "version": "5.0.1", "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", @@ -3055,9 +2947,9 @@ } }, "node_modules/ts-api-utils": { - "version": "2.1.0", - "resolved": "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-2.1.0.tgz", - "integrity": "sha512-CUgTZL1irw8u29bzrOD/nH85jqyc74D6SshFgujOIA7osm2Rz7dYH77agkx7H4FBNxDq7Cjf+IjaX/8zwFW+ZQ==", + "version": "2.4.0", + "resolved": "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-2.4.0.tgz", + "integrity": "sha512-3TaVTaAv2gTiMB35i3FiGJaRfwb3Pyn/j3m/bfAvGe8FB7CF6u+LMYqYlDh7reQf7UNvoTvdfAqHGmPGOSsPmA==", "dev": true, "license": "MIT", "engines": { @@ -3086,7 +2978,6 @@ "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==", "dev": true, "license": "Apache-2.0", - "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" diff --git a/masfactory-visualizer/webview-ui/package-lock.json b/masfactory-visualizer/webview-ui/package-lock.json index 760581c..b01a4ad 100644 --- a/masfactory-visualizer/webview-ui/package-lock.json +++ b/masfactory-visualizer/webview-ui/package-lock.json @@ -1008,7 +1008,6 @@ "resolved": "https://registry.npmjs.org/cytoscape/-/cytoscape-3.33.1.tgz", "integrity": "sha512-iJc4TwyANnOGR1OmWhsS9ayRS3s+XQ185FmuHObThD+5AeJCakAAbWv8KimMTt08xCCLNgneQwFp+JRJOr9qGQ==", "license": "MIT", - "peer": true, "engines": { "node": ">=0.10" } @@ -1334,7 +1333,6 @@ "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==", "devOptional": true, "license": "Apache-2.0", - "peer": true, "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" @@ -1349,7 +1347,6 @@ "integrity": "sha512-o5a9xKjbtuhY6Bi5S3+HvbRERmouabWbyUcpXXUA1u+GNUKoROi9byOJ8M0nHbHYHkYICiMlqxkg1KkYmm25Sw==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "esbuild": "^0.21.3", "postcss": "^8.4.43", @@ -1416,7 +1413,6 @@ "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.26.tgz", "integrity": "sha512-SJ/NTccVyAoNUJmkM9KUqPcYlY+u8OVL1X5EW9RIs3ch5H2uERxyyIUI4MRxVCSOiEcupX9xNGde1tL9ZKpimA==", "license": "MIT", - "peer": true, "dependencies": { "@vue/compiler-dom": "3.5.26", "@vue/compiler-sfc": "3.5.26", diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..2972e9e --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "MASFactory", + "lockfileVersion": 3, + "requires": true, + "packages": {} +}