From 4e3644090f8cb68a2a637d086b2c8c9cca9dbf4b Mon Sep 17 00:00:00 2001 From: ApGa Date: Mon, 16 Mar 2026 04:09:02 +0000 Subject: [PATCH 1/8] feat: Progress on improving recursive agent inference for Oolong --- platoon/agents/actions/subagent.py | 80 ++++---- platoon/agents/codeact/agent.py | 49 ++++- platoon/envs/codeact/env.py | 133 ++++++++++--- platoon/episode/loop.py | 23 ++- platoon/utils/async_hang_debug.py | 174 +++++++++++++++++ platoon/utils/llm_client.py | 83 ++++---- platoon/utils/span_profile.py | 89 +++++++++ platoon/visualization/tui.py | 13 +- ...pworld_ctx40000_4b-recursive-extended.yaml | 182 +++++++++++++++++ .../areal/appworld_ctx40000_4b_linear.yaml | 178 +++++++++++++++++ plugins/appworld/platoon/appworld/env.py | 2 +- plugins/oolong/platoon/oolong/agent.py | 32 ++- .../configs/inference/oolong_inference.yaml | 21 +- .../configs/train/areal/oolong_areal.yaml | 17 +- plugins/oolong/platoon/oolong/env.py | 184 +++++++++++++++--- .../oolong/inference_scripts/run_inference.py | 16 +- plugins/oolong/platoon/oolong/rollout.py | 2 +- plugins/oolong/platoon/oolong/tasks.py | 119 ++++++++--- .../oolong/train_scripts/areal/train_areal.py | 50 +++-- plugins/oolong/pyproject.toml | 1 + plugins/oolong/uv.lock | 136 +++++-------- uv.lock | 90 ++++----- 22 files changed, 1323 insertions(+), 351 deletions(-) create mode 100644 platoon/utils/async_hang_debug.py create mode 100644 platoon/utils/span_profile.py create mode 100644 plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive-extended.yaml create mode 100644 plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b_linear.yaml diff --git a/platoon/agents/actions/subagent.py b/platoon/agents/actions/subagent.py index 32a92de..2d5dd7e 100644 --- a/platoon/agents/actions/subagent.py +++ b/platoon/agents/actions/subagent.py @@ -1,14 +1,15 @@ import asyncio -from typing import cast +from typing import Any, cast from platoon.agents.base import ForkableAgent from platoon.envs.base import ForkableEnv -from platoon.episode.context import budget_tracker, current_agent, current_env, episode_step_timeout +from platoon.episode.context import budget_tracker, current_agent, current_env, current_trajectory, episode_step_timeout from platoon.episode.loop import run_episode from platoon.episode.trajectory import BudgetExceededError +from platoon.utils.span_profile import profile_span -async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None = None) -> str: +async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None = None, verbose: bool = True) -> Any: """Launch a subagent to solve a task. Args: @@ -16,42 +17,55 @@ async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None max_steps: The maximum number of steps the subagent can take. Returns: - Returns the answer or finish message for the goal. + Returns the result of the subagent's execution. """ # Cast is safe here: launch_subagent only works in contexts with forkable agents/envs agent = cast(ForkableAgent, current_agent.get()) env = cast(ForkableEnv, current_env.get()) task = env.task - - subtask = task.fork(goal, max_steps, task_misc=task_misc) - forked_agent = await agent.fork(subtask) - forked_env = await env.fork(subtask) - - try: - budget_tracker.get().reserve_budget(max_steps + 1, raise_on_failure=True) - except (BudgetExceededError, ValueError) as e: - guidance = getattr(e, "guidance", "") - msg = f"Not enough budget to launch subagent for goal {goal}. {e}" - if guidance: - msg += " " + guidance - return msg - - traj = await asyncio.create_task( - run_episode( - forked_agent, - forked_env, - timeout=episode_step_timeout.get(), - ) - ) + parent_traj = current_trajectory.get() + async with profile_span( + "launch_subagent", + metadata={ + "goal_len": len(goal), + "max_steps": max_steps, + "parent_task_id": getattr(task, "id", None), + "parent_trajectory_id": parent_traj.id, + }, + ): + subtask = task.fork(goal, max_steps, task_misc=task_misc) + forked_agent = await agent.fork(subtask) + forked_env = await env.fork(subtask) + + try: + budget_tracker.get().reserve_budget(max_steps + 1, raise_on_failure=True) + except (BudgetExceededError, ValueError) as e: + guidance = getattr(e, "guidance", "") + msg = f"Not enough budget to launch subagent for goal {goal}. {e}" + if guidance: + msg += " " + guidance + return msg - budget_tracker.get().release_budget(max_steps + 1) + try: + traj = await asyncio.create_task( + run_episode( + forked_agent, + forked_env, + timeout=episode_step_timeout.get(), + ) + ) + finally: + budget_tracker.get().release_budget(max_steps + 1) - used_recursive = int(budget_tracker.get().used_budget_for(traj.id)) - remaining_total = int(budget_tracker.get().remaining_budget()) + used_recursive = int(budget_tracker.get().used_budget_for(traj.id)) + remaining_total = int(budget_tracker.get().remaining_budget()) - budget_message = ( - f"\n\nBudget used by subagent: {used_recursive}/{max_steps} steps. " - f"Total remaining budget for the current task is {remaining_total} steps.\n" - ) + budget_message = ( + f"\n\nBudget used by subagent: {used_recursive}/{max_steps} steps. " + f"Total remaining budget for the current task is {remaining_total} steps.\n" + ) - return (traj.finish_message or traj.error_message or "") + budget_message + if verbose: + return (traj.finish_message or traj.error_message or "") + budget_message + else: + return traj.finish_message or traj.error_message or "" diff --git a/platoon/agents/codeact/agent.py b/platoon/agents/codeact/agent.py index c6413e0..1d0c84c 100644 --- a/platoon/agents/codeact/agent.py +++ b/platoon/agents/codeact/agent.py @@ -1,6 +1,7 @@ from __future__ import annotations import re +import uuid from typing import cast from openai.types.chat import ChatCompletionMessageParam @@ -9,7 +10,10 @@ from platoon.agents.codeact.prompt_builder import CodeActPromptBuilder, PromptMode from platoon.envs.base import Task from platoon.envs.codeact import CodeActAction, CodeActObservation +from platoon.episode.context import current_trajectory +from platoon.utils import async_hang_debug from platoon.utils.llm_client import LLMClient +from platoon.utils.span_profile import profile_span def extract_code_and_thought(raw_action: str) -> tuple[str, str]: @@ -125,6 +129,8 @@ async def act(self, obs: CodeActObservation) -> CodeActAction: return self._stuck_in_loop_action() prompt = cast(list[ChatCompletionMessageParam], self.prompt_builder.build_messages(obs)) + current_traj = current_trajectory.get(None) + request_id = str(uuid.uuid4()) request_kwargs = { "stop": [""], "max_completion_tokens": self.inference_params.max_completion_tokens, @@ -135,11 +141,46 @@ async def act(self, obs: CodeActObservation) -> CodeActAction: if self.inference_params.top_p is not None: request_kwargs["top_p"] = self.inference_params.top_p - response = await self.llm_client.async_chat_completion( - prompt, - # Stop sequence is agent-level behavior, not a global rollout knob. - **request_kwargs, + async_hang_debug.track_current_task( + request_id=request_id, + kind="agent_llm", + metadata={ + "task_id": obs.task.id, + "trajectory_id": current_traj.id if current_traj is not None else None, + "parent_trajectory_id": ( + current_traj.parent_info.id + if current_traj is not None and current_traj.parent_info is not None + else None + ), + "step_index": len(obs.history), + "message_count": len(prompt), + "model": getattr(self.llm_client, "model", None), + "timeout": request_kwargs["timeout"], + }, ) + try: + async with profile_span( + "agent_act", + metadata={ + "task_id": obs.task.id, + "trajectory_id": current_traj.id if current_traj is not None else None, + "parent_trajectory_id": ( + current_traj.parent_info.id + if current_traj is not None and current_traj.parent_info is not None + else None + ), + "step_index": len(obs.history), + "message_count": len(prompt), + "model": getattr(self.llm_client, "model", None), + }, + ): + response = await self.llm_client.async_chat_completion( + prompt, + # Stop sequence is agent-level behavior, not a global rollout knob. + **request_kwargs, + ) + finally: + async_hang_debug.untrack(request_id) response_text = response.choices[0].message.content or "" # NOTE: We only do this conditionally, because with Areal, stop words are not supported. # And so we might already have the stop word in the response. diff --git a/platoon/envs/codeact/env.py b/platoon/envs/codeact/env.py index e0ffcbb..1a2f79a 100644 --- a/platoon/envs/codeact/env.py +++ b/platoon/envs/codeact/env.py @@ -19,6 +19,7 @@ finish_message, ) from platoon.utils.ipython_shell import ShellCapture, strip_ansi_escape_sequences +from platoon.utils.span_profile import profile_span from .types import ( CodeActAction, @@ -76,24 +77,34 @@ async def evaluate(self) -> tuple[float, dict]: return 0.0, {} async def step(self, action: CodeActAction) -> CodeActObservation: - step = await self._code_executor.run(action.parsed_code) - - if finish_message.get(None) is not None or error_message.get(None) is not None: - self._state.finished = True - self._state.misc["finish_message"] = finish_message.get() - - step.thought = action.parsed_thought - step.reward, reward_info = await self.evaluate() - step.misc["action_misc"] = action.misc - step.misc["reward_misc"] = reward_info - self._state.reward += step.reward - self._state.history.append(step) - - traj_collection = current_trajectory_collection.get() traj = current_trajectory.get() - traj_collection.add_trajectory_step(traj.id, self._state.history[-1]) - if self._state.finished: - traj.reward = self._state.reward + async with profile_span( + "env_step", + metadata={ + "task_id": self._state.task.id, + "trajectory_id": traj.id, + "parent_trajectory_id": traj.parent_info.id if traj.parent_info is not None else None, + "step_index": len(self._state.history), + "code_len": len(action.parsed_code or ""), + }, + ): + step = await self._code_executor.run(action.parsed_code) + + if finish_message.get(None) is not None or error_message.get(None) is not None: + self._state.finished = True + self._state.misc["finish_message"] = finish_message.get() + + step.thought = action.parsed_thought + step.reward, reward_info = await self.evaluate() + step.misc["action_misc"] = action.misc + step.misc["reward_misc"] = reward_info + self._state.reward += step.reward + self._state.history.append(step) + + traj_collection = current_trajectory_collection.get() + traj_collection.add_trajectory_step(traj.id, self._state.history[-1]) + if self._state.finished: + traj.reward = self._state.reward return await self.observe() @@ -264,6 +275,50 @@ def visit_Call(self, node: ast.Call) -> None: self.generic_visit(node) +class WhileLoopDetector(ast.NodeVisitor): + """AST visitor to detect while loops before execution. + + While loops are easy for the model to misuse in ways that block the single + in-process event loop forever. Prefer bounded `for` loops or iteration over + concrete collections/ranges instead. + """ + + def __init__(self): + self.errors: list[str] = [] + + def visit_While(self, node: ast.While) -> None: + condition_src = ast.unparse(node.test) if hasattr(ast, "unparse") else "" + self.errors.append( + "ERROR: `while` loops are not allowed in this environment because they can hang the shared " + "Python executor. Consider rewriting this as a bounded `for` loop (for example, `for i in range(...)`) " + f"or iterate over a concrete collection instead. Detected condition: `{condition_src}`" + ) + self.generic_visit(node) + + +class InteractiveInputDetector(ast.NodeVisitor): + """AST visitor to detect blocking interactive input calls before execution.""" + + INTERACTIVE_CALLS = {"input"} + + def __init__(self): + self.errors: list[str] = [] + + def visit_Call(self, node: ast.Call) -> None: + func_name = None + if isinstance(node.func, ast.Name): + func_name = node.func.id + elif isinstance(node.func, ast.Attribute): + func_name = node.func.attr + + if func_name in self.INTERACTIVE_CALLS: + self.errors.append( + "ERROR: Interactive input functions like `input()` are not allowed in this environment because " + "they block the shared Python executor waiting for terminal input that will never arrive." + ) + self.generic_visit(node) + + class IPythonCodeExecutor(CodeExecutor): # TODO: Separate actions and modules? Use this info to build action space description? def __init__( @@ -271,12 +326,16 @@ def __init__( task: Task, actions: tuple[Callable[..., object], ...] | Sequence[Callable[..., object]] = (finish, safe_asyncio), detect_unawaited_async_calls: bool = True, + detect_while_loops: bool = False, + detect_interactive_input: bool = False, ): self.task = task self.actions = actions self.shell = self._create_shell() # self.timeout_seconds = timeout_seconds self.detect_unawaited_async_calls = detect_unawaited_async_calls + self.detect_while_loops = detect_while_loops + self.detect_interactive_input = detect_interactive_input def _create_shell(self) -> InteractiveShellEmbed: original_excepthook = sys.excepthook @@ -331,17 +390,39 @@ async def run(self, code: str) -> CodeActStep: if detector.errors: return CodeActStep(code=code, error="\n".join(detector.errors)) - with ShellCapture() as capture: - await self.shell.run_cell_async(code) + if self.detect_while_loops: + detector = WhileLoopDetector() + detector.visit(tree) + if detector.errors: + return CodeActStep(code=code, error="\n".join(detector.errors)) - cap_stdout = strip_ansi_escape_sequences(capture.pop_stdout()) - cap_stderr = strip_ansi_escape_sequences(capture.pop_stderr()) + if self.detect_interactive_input: + detector = InteractiveInputDetector() + detector.visit(tree) + if detector.errors: + return CodeActStep(code=code, error="\n".join(detector.errors)) - # TODO: This might cause unexpected filtering of outputs. - # Guard against empty stdout before indexing first line - first_line = cap_stdout.splitlines()[0] if cap_stdout.splitlines() else "" - if cap_stdout.startswith("Out[") or ("[?7hOut[1]:" in first_line): - cap_stdout = "".join(cap_stdout.split(":")[1:]) + traj = current_trajectory.get() + async with profile_span( + "code_executor_run", + metadata={ + "task_id": self.task.id, + "trajectory_id": traj.id, + "parent_trajectory_id": traj.parent_info.id if traj.parent_info is not None else None, + "code_len": len(code), + }, + ): + with ShellCapture() as capture: + await self.shell.run_cell_async(code) + + cap_stdout = strip_ansi_escape_sequences(capture.pop_stdout()) + cap_stderr = strip_ansi_escape_sequences(capture.pop_stderr()) + + # TODO: This might cause unexpected filtering of outputs. + # Guard against empty stdout before indexing first line + first_line = cap_stdout.splitlines()[0] if cap_stdout.splitlines() else "" + if cap_stdout.startswith("Out[") or ("[?7hOut[1]:" in first_line): + cap_stdout = "".join(cap_stdout.split(":")[1:]) return CodeActStep( code=code, diff --git a/platoon/episode/loop.py b/platoon/episode/loop.py index 456fd83..6ebdbb2 100644 --- a/platoon/episode/loop.py +++ b/platoon/episode/loop.py @@ -14,6 +14,7 @@ finish_message, ) from platoon.episode.trajectory import StepBudgetTracker, Trajectory, TrajectoryCollection +from platoon.utils.span_profile import profile_span # NOTE: Call using asyncio.create_task() to make sure edits to contextvars do not leak to parent context @@ -21,11 +22,23 @@ async def run_episode(agent: Agent, env: Env, verbose: bool = False, timeout: in try: step_count = 0 set_context_vars(agent, env, timeout=timeout) - obs = await env.reset() - while not halt_episode(obs): - action = await asyncio.wait_for(agent.act(obs), timeout=timeout) - obs = await asyncio.wait_for(env.step(action), timeout=timeout) - step_count += 1 + traj = current_trajectory.get() + async with profile_span( + "run_episode", + metadata={ + "agent_type": type(agent).__name__, + "env_type": type(env).__name__, + "task_id": getattr(env.task, "id", None), + "timeout": timeout, + "trajectory_id": traj.id, + "parent_trajectory_id": traj.parent_info.id if traj.parent_info is not None else None, + }, + ): + obs = await env.reset() + while not halt_episode(obs): + action = await asyncio.wait_for(agent.act(obs), timeout=timeout) + obs = await asyncio.wait_for(env.step(action), timeout=timeout) + step_count += 1 except (Exception, asyncio.CancelledError) as e: tb_summary = traceback.extract_tb(e.__traceback__) origin = "" diff --git a/platoon/utils/async_hang_debug.py b/platoon/utils/async_hang_debug.py new file mode 100644 index 0000000..20046da --- /dev/null +++ b/platoon/utils/async_hang_debug.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import asyncio +import logging +import os +import sys +import threading +import time +import traceback +import weakref +from dataclasses import dataclass, field +from typing import Any + +logger = logging.getLogger(__name__) + + +def enabled() -> bool: + return os.getenv("PLATOON_DEBUG_HANGS", "").lower() in {"1", "true", "yes", "on"} + + +def _threshold_sec() -> float: + value = os.getenv("PLATOON_DEBUG_HANG_THRESHOLD_SEC", "60") + try: + return max(float(value), 1.0) + except ValueError: + return 60.0 + + +def _interval_sec() -> float: + value = os.getenv("PLATOON_DEBUG_HANG_INTERVAL_SEC", "15") + try: + return max(float(value), 1.0) + except ValueError: + return 15.0 + + +def _max_tasks() -> int: + value = os.getenv("PLATOON_DEBUG_HANG_MAX_TASKS", "3") + try: + return max(int(value), 1) + except ValueError: + return 3 + + +def _max_frames() -> int: + value = os.getenv("PLATOON_DEBUG_HANG_MAX_FRAMES", "8") + try: + return max(int(value), 1) + except ValueError: + return 8 + + +@dataclass +class _TrackedTask: + request_id: str + kind: str + task_ref: weakref.ReferenceType[asyncio.Task[Any]] + thread_ident: int | None + started_at: float + metadata: dict[str, Any] = field(default_factory=dict) + last_dump_at: float = 0.0 + + +_TRACKED: dict[str, _TrackedTask] = {} +_TRACKED_LOCK = threading.Lock() +_WATCHDOG_THREAD: threading.Thread | None = None +_STOP_EVENT = threading.Event() + + +def track_current_task(request_id: str, kind: str, metadata: dict[str, Any] | None = None) -> None: + if not enabled(): + return + + task = asyncio.current_task() + if task is None: + return + + with _TRACKED_LOCK: + _TRACKED[request_id] = _TrackedTask( + request_id=request_id, + kind=kind, + task_ref=weakref.ref(task), + thread_ident=threading.get_ident(), + started_at=time.monotonic(), + metadata=dict(metadata or {}), + ) + _ensure_watchdog_thread() + + +def untrack(request_id: str) -> None: + if not enabled(): + return + + with _TRACKED_LOCK: + _TRACKED.pop(request_id, None) + + +def _ensure_watchdog_thread() -> None: + global _WATCHDOG_THREAD + if _WATCHDOG_THREAD is not None and _WATCHDOG_THREAD.is_alive(): + return + _STOP_EVENT.clear() + _WATCHDOG_THREAD = threading.Thread( + target=_watchdog_loop, + name="platoon-hang-watchdog", + daemon=True, + ) + _WATCHDOG_THREAD.start() + + +def _watchdog_loop() -> None: + while True: + if _STOP_EVENT.wait(_interval_sec()): + return + + with _TRACKED_LOCK: + tracked = dict(_TRACKED) + if not tracked: + _STOP_EVENT.set() + return + + now = time.monotonic() + stale: list[_TrackedTask] = [] + expired_ids: list[str] = [] + for request_id, record in tracked.items(): + task = record.task_ref() + if task is None or task.done(): + expired_ids.append(request_id) + continue + if now - record.started_at >= _threshold_sec(): + stale.append(record) + + if expired_ids: + with _TRACKED_LOCK: + for request_id in expired_ids: + current = _TRACKED.get(request_id) + if current is not None and (current.task_ref() is None or current.task_ref().done()): + _TRACKED.pop(request_id, None) + + stale.sort(key=lambda record: record.started_at) + for record in stale[: _max_tasks()]: + if now - record.last_dump_at < _interval_sec() * 0.9: + continue + _log_task_stack(record, now) + with _TRACKED_LOCK: + current = _TRACKED.get(record.request_id) + if current is not None: + current.last_dump_at = now + + +def _log_task_stack(record: _TrackedTask, now: float) -> None: + task = record.task_ref() + if task is None: + return + + formatted_stack: list[str] = [] + if record.thread_ident is not None: + frame = sys._current_frames().get(record.thread_ident) + if frame is not None: + formatted_stack = traceback.format_stack(frame, limit=_max_frames()) + + waiter = getattr(task, "_fut_waiter", None) + logger.warning( + "hang_watchdog kind=%s request_id=%s age_s=%.1f thread_ident=%s task_name=%s task_coro=%r waiter=%r metadata=%s\n%s", + record.kind, + record.request_id, + now - record.started_at, + record.thread_ident, + task.get_name(), + task.get_coro(), + waiter, + record.metadata, + "".join(formatted_stack).rstrip() or "", + ) diff --git a/platoon/utils/llm_client.py b/platoon/utils/llm_client.py index 9254ac5..ac788ac 100644 --- a/platoon/utils/llm_client.py +++ b/platoon/utils/llm_client.py @@ -1,13 +1,15 @@ from __future__ import annotations +import asyncio import os -import re from typing import Any, TypeAlias, TypedDict, cast import litellm from openai import AsyncOpenAI, OpenAI from openai.types.chat import ChatCompletion, ChatCompletionMessageParam +from platoon.utils.span_profile import profile_span + class ChatMessage(TypedDict): role: str @@ -32,8 +34,6 @@ def _get_litellm_semaphore(): Disabled by default so existing workloads keep current behavior unless `PLATOON_LITELLM_MAX_INFLIGHT` is explicitly set. """ - import asyncio - global _LITELLM_SEMAPHORE, _LITELLM_SEMAPHORE_PID limit_str = os.getenv("PLATOON_LITELLM_MAX_INFLIGHT") @@ -127,6 +127,7 @@ def chat_completion( message["content"] = [{"type": "text", "text": message["content"]}] messages[-1]["content"][-1]["cache_control"] = {"type": "ephemeral"} + inflight = 0 try: response: ChatCompletion = self.client.chat.completions.create( model=self.model, @@ -347,18 +348,6 @@ def __init__(self, model: str, base_url: str | None = None, api_key: str | None self.base_url = base_url self.api_key = api_key - @staticmethod - def _sanitize_error_message(err: Exception, max_len: int = 800) -> str: - """Collapse oversized payload dumps in exception text to keep logs readable.""" - msg = str(err) - # Replace huge token dumps like {'input_ids': [...]} with a short marker. - msg = re.sub(r"'input_ids':\s*\[[^\]]*\]", "'input_ids': []", msg) - # Avoid printing entire request payload blobs when providers include them in errors. - msg = re.sub(r"Payload:\s*\{.*\}\s*$", "Payload: ", msg, flags=re.DOTALL) - if len(msg) > max_len: - msg = f"{msg[:max_len]}... [truncated]" - return msg - async def async_chat_completion( self, messages: list[ChatCompletionMessageParam], @@ -382,7 +371,6 @@ async def async_chat_completion( Raises: Exception: If the API call fails. """ - if auto_add_cache_control: for message in messages: if isinstance(message["content"], str): @@ -392,45 +380,52 @@ async def async_chat_completion( # Disable LiteLLM internal retries by default to prevent long rollout slot blocking. # Callers can still override by explicitly passing num_retries in kwargs. kwargs.setdefault("num_retries", 0) - try: - semaphore = _get_litellm_semaphore() - if semaphore is None: - response = await litellm.acompletion( - model=self.model, - api_base=self.base_url, - api_key=self.api_key, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - **kwargs, - ) - else: - async with semaphore: - response = await litellm.acompletion( - model=self.model, - api_base=self.base_url, - api_key=self.api_key, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - **kwargs, + async with profile_span( + "llm_chat_completion", + metadata={ + "message_count": len(messages), + "max_tokens": max_tokens, + "model": self.model, + "semaphore_enabled": _get_litellm_semaphore() is not None, + "temperature": temperature, + "timeout": kwargs.get("timeout"), + }, + ): + semaphore = _get_litellm_semaphore() + + async def _do_request() -> ChatCompletion: + return cast( + ChatCompletion, + await litellm.acompletion( + model=self.model, + api_base=self.base_url, + api_key=self.api_key, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + **kwargs, + ), ) + if semaphore is None: + response = await _do_request() + else: + async with semaphore: + response = await _do_request() + if not response.choices: print("No response choices received from LiteLLM") raise Exception("No response choices received from LiteLLM") - # LiteLLM returns ModelResponse, convert to ChatCompletion format - return cast(ChatCompletion, response) + return response except Exception as e: - sanitized = self._sanitize_error_message(e) - print(f"LiteLLMClient async_chat_completion failed: {sanitized}") - raise RuntimeError(f"LiteLLM API call failed: {sanitized}") from e + print(f"LiteLLMClient async_chat_completion failed: {e}") + raise RuntimeError(f"LiteLLM API call failed: {e}") from e async def aclose(self) -> None: - """Close the client connection (no-op for LiteLLM).""" + """Close the client connection.""" pass def fork(self) -> "LiteLLMClient": diff --git a/platoon/utils/span_profile.py b/platoon/utils/span_profile.py new file mode 100644 index 0000000..5f53a77 --- /dev/null +++ b/platoon/utils/span_profile.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import asyncio +import contextvars +import json +import os +import threading +import time +import uuid +from contextlib import asynccontextmanager +from dataclasses import dataclass +from datetime import datetime, timezone +from typing import Any, AsyncIterator + +_WRITE_LOCK = threading.Lock() +_SPAN_STACK: contextvars.ContextVar[tuple["_SpanState", ...]] = contextvars.ContextVar( + "platoon_span_profile_stack", + default=(), +) + + +def enabled() -> bool: + return os.getenv("PLATOON_PROFILE_SPANS", "").lower() in {"1", "true", "yes", "on"} + + +def output_path() -> str: + return os.getenv("PLATOON_PROFILE_SPANS_PATH", "/tmp/platoon_span_profile.jsonl") + + +@dataclass +class _SpanState: + span_id: str + name: str + started_at: float + parent_span_id: str | None + metadata: dict[str, Any] + child_time_ms: float = 0.0 + + +def _append_record(record: dict[str, Any]) -> None: + path = output_path() + os.makedirs(os.path.dirname(path) or ".", exist_ok=True) + with _WRITE_LOCK: + with open(path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, sort_keys=True) + "\n") + + +@asynccontextmanager +async def profile_span(name: str, metadata: dict[str, Any] | None = None) -> AsyncIterator[str | None]: + """Record a hierarchical async wall-time span when profiling is enabled.""" + if not enabled(): + yield None + return + + stack = _SPAN_STACK.get() + parent = stack[-1] if stack else None + state = _SpanState( + span_id=str(uuid.uuid4()), + name=name, + started_at=time.perf_counter(), + parent_span_id=parent.span_id if parent is not None else None, + metadata=dict(metadata or {}), + ) + token = _SPAN_STACK.set((*stack, state)) + try: + yield state.span_id + finally: + ended_at = time.perf_counter() + inclusive_ms = (ended_at - state.started_at) * 1000.0 + self_ms = max(0.0, inclusive_ms - state.child_time_ms) + _SPAN_STACK.reset(token) + parent_stack = _SPAN_STACK.get() + if parent_stack: + parent_stack[-1].child_time_ms += inclusive_ms + + task = asyncio.current_task() + _append_record( + { + "ended_at": datetime.now(timezone.utc).isoformat(), + "inclusive_ms": round(inclusive_ms, 3), + "metadata": state.metadata, + "name": state.name, + "parent_span_id": state.parent_span_id, + "pid": os.getpid(), + "self_ms": round(self_ms, 3), + "span_id": state.span_id, + "task_name": task.get_name() if task is not None else None, + } + ) diff --git a/platoon/visualization/tui.py b/platoon/visualization/tui.py index dafb11f..d1cacb7 100644 --- a/platoon/visualization/tui.py +++ b/platoon/visualization/tui.py @@ -503,7 +503,7 @@ def __init__(self) -> None: self.step_nodes: Dict[tuple[str, int], TreeNode[str]] = {} # Track the latest known reward per trajectory for quick label updates self.traj_rewards: Dict[str, float] = {} - # Track whether a trajectory is finished (finish_message or error_message present) + # Track whether a trajectory has reached a terminal state. self.traj_finished: Dict[str, bool] = {} # Track which trajectories are currently expanded (for efficient collapse_all_except) self.expanded_trajs: set[str] = set() @@ -874,11 +874,11 @@ def ingest(self, event: Event) -> None: self.traj_rewards[traj_id] = float(traj.get("reward", 0.0)) except Exception: self.traj_rewards[traj_id] = 0.0 - # Initialize finished status from payload if present + # Initialize terminal status from payload if present. try: finish_msg = traj.get("finish_message") error_msg = traj.get("error_message") - self.traj_finished[traj_id] = bool(finish_msg) or bool(error_msg) + self.traj_finished[traj_id] = finish_msg is not None or error_msg is not None except Exception: self.traj_finished[traj_id] = False label = self._format_traj_label(traj_id) @@ -928,9 +928,9 @@ def ingest(self, event: Event) -> None: payload["finish_message"] = finish_msg if error_msg is not None: payload["error_message"] = error_msg - # Update finished map if we received any terminal info + # Update terminal status if we received any terminal info. if finish_msg is not None or error_msg is not None: - self.traj_finished[traj_id] = bool(finish_msg) or bool(error_msg) + self.traj_finished[traj_id] = True except Exception: pass # Update the trajectory node label to reflect latest reward @@ -996,7 +996,8 @@ def ingest(self, event: Event) -> None: payload["finish_message"] = finish_msg if error_msg is not None: payload["error_message"] = error_msg - self.traj_finished[traj_id] = bool(finish_msg) or bool(error_msg) + # A trajectory_finished event is authoritative even when finish_message == "". + self.traj_finished[traj_id] = True except Exception: pass # Refresh label to reflect final reward and status diff --git a/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive-extended.yaml b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive-extended.yaml new file mode 100644 index 0000000..cc102d6 --- /dev/null +++ b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive-extended.yaml @@ -0,0 +1,182 @@ +experiment_name: recursive-agents +trial_name: appworld-ctx40000-cispo-4b-mixed-sft-recursive-trial9 + +seed: 343 +total_train_epochs: 100 +tokenizer_path: ${actor.path} +depth_aware: true + + +cluster: + n_nodes: 1 + n_gpus_per_node: 8 + fileroot: /mnt/efs/tmp/areal/experiments + name_resolve: + type: nfs + nfs_record_root: /mnt/efs/tmp/areal/name_resolve + +allocation_mode: sglang:d4p1t1+d4p1t1c1 + +rollout: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + max_concurrent_rollouts: 16 + queue_size: null + consumer_batch_size: ${train_dataset.batch_size} + max_head_offpolicyness: 3 + enable_rollout_tracing: false + +gconfig: + n_samples: 1 + min_new_tokens: 0 + max_new_tokens: 8192 + greedy: false + temperature: 1.0 + +workflow_config: + rollout_config: + model_name: ${actor.path} + max_steps: 75 + output_dir: /mnt/efs/tmp/areal/experiments/appworld-ctx40000-cispo-4b-mixed-sft-recursive-trial9 + verbose: True + train: true + timeout: 7200 + step_timeout: 7200 + inference_params: + temperature: 1.0 + max_completion_tokens: 2048 + group_size: 8 + use_subprocesses: true + leave_one_out_baseline: True # Use leave-one-out baseline for advantage centering + depth_level_weighting: True # Weight trajectories inversely by depth-level frequency + +# CISPO loss function configuration (matches tinker settings) +loss_fn_config: + loss_fn: cispo + clip_low_threshold: 0.0 + clip_high_threshold: 5.0 + +actor: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: apurvaga/qwen3-4b-sft-mixed-appworld #/mnt/efs/data/echoagent/llama-factory/saves/appworld-sft-qwen3-4b-recursive_opus_4_5 + init_from_scratch: false + disable_dropout: true + gradient_checkpointing: true + dtype: bfloat16 + mb_spec: + max_tokens_per_mb: 70000 + optimizer: + type: adam + lr: 3e-6 + weight_decay: 0.017 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + lr_scheduler_type: constant + gradient_clipping: 1.0 + warmup_steps_proportion: 0.001 + group_size: ${gconfig.n_samples} + eps_clip: 0.2 + eps_clip_higher: 0.26 + temperature: ${gconfig.temperature} + reward_scaling: 1.0 + reward_bias: 0.0 + kl_ctl: 0.0 + kl_estimator: k1 + ppo_n_minibatches: 1 + recompute_logprob: false #true + use_decoupled_loss: false #true + behav_imp_weight_cap: null + dynamic_sampling: false + reward_norm: + mean_level: null + std_level: null + adv_norm: + mean_level: null + std_level: null + max_new_tokens: ${gconfig.max_new_tokens} + # lora + use_lora: false + peft_type: lora + lora_rank: 32 + lora_alpha: 16 + target_modules: [all-linear] + +ref: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: ${actor.path} + init_from_scratch: false + disable_dropout: true + dtype: ${actor.dtype} + mb_spec: + max_tokens_per_mb: 4000 + optimizer: null + +# SGLang +sglang: + model_path: ${actor.path} + random_seed: ${seed} + skip_tokenizer_init: false + dtype: ${actor.dtype} + max_running_requests: null + context_length: 40001 + mem_fraction_static: 0.8 + enable_lora: ${actor.use_lora} + max_lora_rank: ${actor.lora_rank} + lora_target_modules: ${actor.target_modules} + #attention_backend: flashinfer # Uncomment for blackwell GPUs + +# datasets +train_dataset: + batch_size: 16 + type: rl + path: "" + +valid_dataset: + batch_size: 50 + type: rl + path: "" + +# Utilities +saver: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 25 + freq_secs: null + +recover: + mode: auto + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: 1 + freq_steps: 5 + freq_secs: 3600 + +evaluator: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 20 + freq_secs: null + +stats_logger: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + wandb: + mode: online + project: recursive-agents + +launcher: + inference_server_cpus_per_gpu: 32 + inference_server_mem_per_gpu: 32768 + trainer_cpus_per_gpu: 4 + trainer_mem_per_gpu: 32768 + #trainer_env_vars: PLATOON_LITELLM_MAX_INFLIGHT=16 + diff --git a/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b_linear.yaml b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b_linear.yaml new file mode 100644 index 0000000..5bf2e81 --- /dev/null +++ b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b_linear.yaml @@ -0,0 +1,178 @@ +experiment_name: recursive-agents +trial_name: appworld-ctx40000-cispo-3.5-4b-linear-trial-0 + +seed: 343 +total_train_epochs: 100 +tokenizer_path: ${actor.path} +recursive: false + + +cluster: + n_nodes: 1 + n_gpus_per_node: 8 + fileroot: /mnt/efs/tmp/areal/experiments + name_resolve: + type: nfs + nfs_record_root: /mnt/efs/tmp/areal/name_resolve + +allocation_mode: sglang:d4p1t1+d2p1t1c2 + +rollout: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + max_concurrent_rollouts: 16 + queue_size: null + consumer_batch_size: ${train_dataset.batch_size} + max_head_offpolicyness: 3 + enable_rollout_tracing: false + +gconfig: + n_samples: 1 + min_new_tokens: 0 + max_new_tokens: 8192 + greedy: false + temperature: 1.0 + +workflow_config: + rollout_config: + model_name: ${actor.path} + max_steps: 75 + output_dir: /mnt/efs/tmp/areal/experiments/appworld-ctx40000-cispo-3.5-4b-linear-trial-0 + verbose: True + train: true + timeout: 900 + step_timeout: 600 + inference_params: + temperature: 1.0 + max_completion_tokens: 2048 + group_size: 8 + use_subprocesses: true + +# CISPO loss function configuration (matches tinker settings) +loss_fn_config: + loss_fn: cispo + clip_low_threshold: 0.0 + clip_high_threshold: 5.0 + +actor: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: Qwen/Qwen3.5-4B + init_from_scratch: false + disable_dropout: true + gradient_checkpointing: true + dtype: bfloat16 + mb_spec: + max_tokens_per_mb: 40000 + optimizer: + type: adam + lr: 3e-6 + weight_decay: 0.017 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + lr_scheduler_type: constant + gradient_clipping: 1.0 + warmup_steps_proportion: 0.001 + group_size: ${gconfig.n_samples} + eps_clip: 0.2 + eps_clip_higher: 0.26 + temperature: ${gconfig.temperature} + reward_scaling: 1.0 + reward_bias: 0.0 + kl_ctl: 0.0 + kl_estimator: k1 + ppo_n_minibatches: 1 + recompute_logprob: false #true + use_decoupled_loss: false #true + behav_imp_weight_cap: null + dynamic_sampling: false + reward_norm: + mean_level: null + std_level: null + adv_norm: + mean_level: null + std_level: null + max_new_tokens: ${gconfig.max_new_tokens} + # lora + use_lora: false + peft_type: lora + lora_rank: 32 + lora_alpha: 16 + target_modules: [all-linear] + +ref: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: ${actor.path} + init_from_scratch: false + disable_dropout: true + dtype: ${actor.dtype} + mb_spec: + max_tokens_per_mb: 4000 + optimizer: null + +# SGLang +sglang: + model_path: ${actor.path} + random_seed: ${seed} + skip_tokenizer_init: false + dtype: ${actor.dtype} + max_running_requests: null + context_length: 40001 + mem_fraction_static: 0.8 + enable_lora: ${actor.use_lora} + max_lora_rank: ${actor.lora_rank} + lora_target_modules: ${actor.target_modules} + #attention_backend: flashinfer # Uncomment for blackwell GPUs + +# datasets +train_dataset: + batch_size: 16 + type: rl + path: "" + +valid_dataset: + batch_size: 50 + type: rl + path: "" + +# Utilities +saver: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 25 + freq_secs: null + +recover: + mode: auto + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: 1 + freq_steps: 5 + freq_secs: 3600 + +evaluator: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 5 + freq_secs: null + +stats_logger: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + wandb: + mode: online + project: recursive-agents + +launcher: + inference_server_cpus_per_gpu: 32 + inference_server_mem_per_gpu: 32768 + trainer_cpus_per_gpu: 4 + trainer_mem_per_gpu: 32768 diff --git a/plugins/appworld/platoon/appworld/env.py b/plugins/appworld/platoon/appworld/env.py index ba2acc7..bd4cfbe 100644 --- a/plugins/appworld/platoon/appworld/env.py +++ b/plugins/appworld/platoon/appworld/env.py @@ -471,7 +471,7 @@ async def run(self, code: str) -> CodeActStep: # Check if evaluation was successful (score > 0) # if reward_misc.get("success", False) or final_step.reward > 0: # succeeded += 1 - succeeded = final_step.reward + succeeded += final_step.reward self._subagent_stats_this_step = (launched, succeeded) return result diff --git a/plugins/oolong/platoon/oolong/agent.py b/plugins/oolong/platoon/oolong/agent.py index 7ce2e75..bc2d333 100644 --- a/plugins/oolong/platoon/oolong/agent.py +++ b/plugins/oolong/platoon/oolong/agent.py @@ -23,10 +23,10 @@ def build_system_prompt(self, obs: CodeActObservation, **context) -> str: CONTEXT ANALYSIS: -- If the length on the context is very large (>30K characters), first examine/peek into the structure of the context (what format is the data in?) +- If the length on the context is very large (>160K characters), first examine/peek into the structure of the context (what format is the data in?) - The context often contains structured data that you can programmatically parse or split: messages with timestamps, users, and content - Use Python string operations to parse, filter, and chunk the data -- For very large contexts, work with chunks rather than the entire context at once +- For very large contexts (i.e., >160K characters), work with chunks rather than the entire context at once - If you are able to programmatically process chunks (e.g., using regex, list comprehensions, etc.), prefer doing this over printing out the chunk/context to inspect it. - But if there is no easy rule-based method to analyze the chunk, then you may have to print it out to observe it. @@ -87,20 +87,18 @@ def build_system_prompt(self, obs: CodeActObservation, **context) -> str: CONTEXT ANALYSIS: -- If the length on the context is very large (>30K characters), first examine/peek into the structure of the context (what format is the data in?) -- The context often contains structured data that you can programmatically parse or split: messages with timestamps, users, and content -- Use Python string operations to parse, filter, and chunk the data -- For very large contexts, work with chunks rather than the entire context at once -- You can process chunks using subagents or programmatic methods. Prefer to use subagents, unless you are confident that the programmatic method will be reliable. -- **IMPORTANT**: Don't just split the context into the smallest atomic pieces and launch subagents for each. There is a cost to launching subagents. Instead, you -should recursively break down context into a small number of larger chunks. If a subagent can process the whole chunk within its context, then it can inspect and directly answer the question. -Do not just delegate to a massive number of subagents upfront. If the size of a chunk is <= 30K characters, then an agent can typically inspect the chunk and answer the question directly. - -RECURSIVE DELEGATION: -- You have the ability to spawn subagents (other instatiations of yourself) with their own `context` and goal. -- Use subagents to process chunks and then aggregate the results to produce a final answer. +- First check if the length on the context is very large (>32K characters) using `len(context)`. +- For very large contexts (i.e., >32K characters), work with chunks rather than the entire context at once. +- Use subagents to process chunks and then aggregate the results to produce a final answer. Try not to split the context into too many chunks (32K characters per chunk is a good rule of thumb) +- If the context <= 32K characters, prefer to process your context by printing out and reading it rather than using programmatic heuristics. +- **IMPORTANT: DO NOT USE regex, string matching, etc. types of programmatic heuristics. It is important to read the context with `print(context)` to be accurate in your answer.** + +SUBAGENT DELEGATION: +- **IMPORTANT: Do not use subagents if the context you need to process is <= 32K characters. Just print out the context to observe it directly and answer the question by reading the context.** +- You have the ability to spawn subagents (other instantiations of yourself), by providing them with their own `context`/chunk to process and a goal/instruction for what result it should return. - You can use asyncio.gather to process multiple chunks simultaneously. -- Subagents can further spawn subagents to process even smaller chunks allowing you to process context using recursive divide-and-conquer. +- Be specific about the format and typein which you expect subagents to return their results. +- Do not provide the context/chunk as part of the goal. Instead, pass it explicitly as the `context` argument to the `launch_subagent` function. ANSWER SUBMISSION: - You can submit your answer using the `finish` function in the format requested in the user provided goal. @@ -110,13 +108,13 @@ def build_system_prompt(self, obs: CodeActObservation, **context) -> str: if include_reasoning: return base_instructions + """ -You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +You can perform printing out or peaking into the context or launching subagents using Python code blocks. You will get multiple steps to complete the task. For your current step, first briefly reason (~1-3 sentences) about your recursive strategy in tags, then output your code in tags. Your code will be executed in a Jupyter notebook and the output will be shown to you.""" else: return base_instructions + """ -You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +You can perform printing out or peaking into the context or launching subagents using Python code blocks. You will get multiple steps to complete the task. Output your code in tags.""" diff --git a/plugins/oolong/platoon/oolong/configs/inference/oolong_inference.yaml b/plugins/oolong/platoon/oolong/configs/inference/oolong_inference.yaml index 13f4d65..5448be3 100644 --- a/plugins/oolong/platoon/oolong/configs/inference/oolong_inference.yaml +++ b/plugins/oolong/platoon/oolong/configs/inference/oolong_inference.yaml @@ -1,37 +1,40 @@ # Oolong inference benchmark config. inference: model_name: openai/Qwen/Qwen3-4B-Instruct-2507 - model_endpoint: http://127.0.0.1:30000/v1 + model_endpoint: http://127.0.0.1:30000/v1 #uv run --extra areal -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --dp 8 --context-length 70000 model_api_key: null - output_dir: ./inference_results/oolong_synth_qwen_4b_recursive + output_dir: /tmp/oolong_both_qwen_4b_recursive resume: true workflow: num_rollouts_per_task: 1 - num_concurrent_workers: 1 + num_concurrent_workers: 10 success_threshold: 1.0 fail_fast: false use_subprocesses: false subprocess_max_workers: null rollout_config: max_steps: 50 - output_dir: ./inference_results/oolong_synth_qwen_4b_recursive/rollouts + output_dir: /tmp/oolong_both_qwen_4b_recursive/rollouts verbose: true - timeout: 7200 - step_timeout: 7200 + timeout: 3600 + step_timeout: 3600 inference_params: temperature: 1.0 top_p: null - max_completion_tokens: 2048 + max_completion_tokens: 8192 + # Dataset configuration -oolong_dataset: synth # synth or real +oolong_dataset: both # synth, real, or both dataset_split: validation # validation or test -num_tasks: 5 +num_tasks: 10 use_recursive_agent: true task_id: null # Filtering options (null means no filter) task_group: null # counting, user, or timeline answer_type: null # NUMERIC, LABEL, COMPARISON, USER, MONTH_YEAR +min_context_len: null +max_context_len: 240000 # full: run rollouts + report # rollouts: only write rollouts to disk (resume-friendly) # report: only read existing rollouts and generate report diff --git a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml index c215cee..66303da 100644 --- a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml +++ b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml @@ -1,10 +1,15 @@ experiment_name: recursive-agents -trial_name: oolong-synth-recursive-trial-0 +trial_name: oolong-real-qwen-4b-recursive-trial-0 seed: 343 total_train_epochs: 10 tokenizer_path: ${actor.path} recursive: true +oolong_dataset: real +task_group: null +answer_type: null +min_context_len: null +max_context_len: 240000 #enable_offload: false @@ -40,15 +45,17 @@ workflow_config: rollout_config: model_name: ${actor.path} max_steps: 200 - output_dir: /mnt/efs/tmp/areal/experiments/oolong-synth-recursive-trial-0 + output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-0 verbose: True train: true - timeout: 7200 - step_timeout: 7200 + timeout: 3600 + step_timeout: 3600 inference_params: temperature: 1.0 - max_completion_tokens: 2048 + max_completion_tokens: 8192 group_size: 8 + depth_level_weighting: true + leave_one_out_baseline: true # CISPO loss function configuration (matches tinker settings) loss_fn_config: diff --git a/plugins/oolong/platoon/oolong/env.py b/plugins/oolong/platoon/oolong/env.py index 5f153b6..c093387 100644 --- a/plugins/oolong/platoon/oolong/env.py +++ b/plugins/oolong/platoon/oolong/env.py @@ -2,7 +2,11 @@ from __future__ import annotations from copy import deepcopy -from rubric.core.checklist import RubricChecklistFast +import json +import os +import re +from rubric.core.checklist import RubricChecklistFast +from rubric.utils.llm_tools import create_llm_client as rubric_create_llm_client from platoon.envs.codeact import CodeActEnv, IPythonCodeExecutor from platoon.envs.base import Task, SubTask @@ -12,6 +16,8 @@ from platoon.envs.codeact import safe_asyncio from platoon.oolong.agent import OolongPromptBuilder from platoon.oolong.eval_helpers import dnd_process_response, synth_process_response +from platoon.utils.span_profile import profile_span + class OolongCodeExecutor(IPythonCodeExecutor): @@ -26,7 +32,9 @@ def __init__(self, task: Task): finish, safe_asyncio ), - detect_unawaited_async_calls=False + detect_unawaited_async_calls=True, + detect_while_loops=True, + detect_interactive_input=True, ) async def describe_action_space(self) -> str: @@ -43,29 +51,35 @@ class OolongRecursiveCodeExecutor(OolongCodeExecutor): def __init__(self, task: Task, subagent_max_steps: int | None = 25): super().__init__(task) self.subagent_max_steps = subagent_max_steps - self.shell.user_ns['context'] = self.task.misc['context'] + self.shell.user_ns['context'] = self.context self.shell.user_ns['launch_subagent'] = self.launch_subagent self._subagent_stats_this_step: tuple[int, int] = (0, 0) async def describe_action_space(self) -> str: return """Available Actions (python functions): -1. async def launch_subagent(goal: str, context: str) -> str +1. async def launch_subagent(goal: str, context: str) -> Any Launch a subagent to process a chunk of the context. - Returns the finish message from the subagent. - - goal: The goal/instruction for the subagent. + Returns the result of the subagent's execution with return type specified in the goal (if specified, else str) + - goal: The goal/instruction for the subagent. Tell the subagent what information you want and specify the exact format and type in which you expect the subagent to return its result. - context: The chunk of the context to process. + Examples: + + result = await launch_subagent(goal="Find the last two spells mentioned in the context by printing out the context and manually reading it. Return the spells as a stringified list of spells that I can parse with json.loads()", context=context_chunk) + print(f"Subagent result: {result}") + + Note: `asyncio` is already imported. Use `await asyncio.gather(...)` to run subtasks in parallel -or `await launch_subagent()` for a single subtask. **Do not forget to await** the results. +or `await launch_subagent()` for a single subtask. **Do not forget to await** the function call. -2. def finish(message: str) -> str - Complete the task with your answer. +2. def finish(result: Any) -> Any + Complete the task with your result. """ async def reset(self) -> OolongRecursiveCodeExecutor: await super().reset() # Re-inject bindings that are lost when the shell is recreated - self.shell.user_ns['context'] = self.task.misc['context'] + self.shell.user_ns['context'] = self.context self.shell.user_ns['launch_subagent'] = self.launch_subagent self._subagent_stats_this_step = (0, 0) return self @@ -78,7 +92,7 @@ def get_subagent_stats(self) -> tuple[int, int]: """Get (launched_count, success_count) for current step.""" return self._subagent_stats_this_step - async def launch_subagent(self, goal: str, context: str) -> str: + async def launch_subagent(self, goal: str, context: str="") -> str: task_misc = deepcopy(self.task.misc) task_misc['context'] = context @@ -91,7 +105,8 @@ async def launch_subagent(self, goal: str, context: str) -> str: result = await launch_subagent( goal=goal, max_steps=self.subagent_max_steps, - task_misc=task_misc + task_misc=task_misc, + verbose=False ) # --- subagent success tracking --- @@ -102,10 +117,7 @@ async def launch_subagent(self, goal: str, context: str) -> str: if traj.parent_info and traj.parent_info.id == current_traj.id: if traj.steps: final_step = traj.steps[-1] - reward_misc = final_step.misc.get("reward_misc", {}) - if reward_misc.get("success", False): - succeeded += 1 - break + succeeded += final_step.reward self._subagent_stats_this_step = (launched, succeeded) return result @@ -120,7 +132,64 @@ async def fork(self, task: Task) -> OolongRecursiveCodeExecutor: class OolongEnv(CodeActEnv): def __init__(self, task: Task): - super().__init__(task, OolongCodeExecutor(task)) + task.fork_strategy = "task" + code_executor = OolongCodeExecutor(task) + # Remove context task misc to avoid massive context logging in events + self.context = task.misc.pop('context') + super().__init__(task, code_executor) + + def _parse_rubric_response(self, response: str) -> dict: + """Parse the LLM response to extract structured data. + + Args: + response: Raw response from the LLM. + + Returns: + Parsed response as dictionary. + """ + try: + # Try to find JSON code block first + json_match = re.search(r"```json\s*(.*?)\s*```", response, re.DOTALL | re.IGNORECASE) + + if json_match: + json_str = json_match.group(1).strip() + else: + # Fallback: look for any ``` code block that might contain JSON + code_match = re.search(r"```\s*(.*?)\s*```", response, re.DOTALL) + if code_match: + json_str = code_match.group(1).strip() + else: + # Last resort: try the entire response as JSON + json_str = response.strip() + + # Parse the JSON response + parsed_response = json.loads(json_str) + + # Validate required fields + if not isinstance(parsed_response, dict): + raise ValueError("Response must be a JSON object") + + required_fields = ["reason", "success"] + for field in required_fields: + if field not in parsed_response: + raise ValueError(f"Missing required field: {field}") + + return parsed_response + + except json.JSONDecodeError as e: + raise ValueError(f"Could not parse JSON response: {str(e)}") from e + + def parse_rubric_response(self, response: str) -> tuple[float, str]: + rubric_dict = self._parse_rubric_response(response) + success_flag = rubric_dict['success'] + if isinstance(success_flag, bool): + score = 1.0 if success_flag else 0.0 + reason = rubric_dict['reason'] + else: + reason = "Success flag is not a boolean value" + score = 0.0 + + return score, reason async def evaluate(self) -> tuple[float, dict]: @@ -128,28 +197,72 @@ async def evaluate(self) -> tuple[float, dict]: reward_misc = {} if self._state.finished: - if isinstance(self._task, SubTask) and self._task.parent_tasks: + if not "oolong" in self._task.id: try: - rubric_checklist = RubricChecklistFast(self._task.goal) + #rubric_checklist = RubricChecklistFast(self._task.goal) prompt_builder = OolongPromptBuilder() action_history = prompt_builder.build_action_history_description(await self.observe()) - # Pull messages from episode-level context vars first; fall back to last step if available - final_message = finish_message.get() or (self._state.history[-1].misc.get("finish_message") if self._state.history else None) - err_message = error_message.get() or (self._state.history[-1].misc.get("error_message") if self._state.history else None) - - rubric_context = f"We need to judge the performance of an agent on the task.\n\n# Agent Trajectory Info\n## Action History\n{action_history}\n\n## Final Message\n{final_message}\n\n## Error Message\n{err_message}" - score, reason = await rubric_checklist.aevaluate(include_reason=True, context=rubric_context) - + # Preserve falsy-but-meaningful outputs like 0 or ""; only fall back on None. + final_message = finish_message.get() + if final_message is None and self._state.history: + final_message = self._state.history[-1].misc.get("finish_message") + err_message = error_message.get() + if err_message is None and self._state.history: + err_message = self._state.history[-1].misc.get("error_message") + + rubric_system_prompt = ( + "We need to judge the performance of an agent on a task. The task relates to aggregating some information from a potentially very large context.\n" + "Read the context carefully and see if the agent's answer is accurate. If the agent accidentally got the correct answer (e.g., by using heuristics that happened to work on the context), then do not mark the agent as successful." + "Please provide a reason and success flag (boolean value) in the following format:\n" + "```json\n" + "{\n" + " \"reason\": \"Brief reasoning for success flag here.\",\n" + " \"success\": \n" + "}\n" + "```" + ) + if self.context: + prompt_start = f"# Task:\n{self._task.goal}\n\n# Context:\n{self.context}" + else: + prompt_start = f"# Task:\n{self._task.goal}" + rubric_client = rubric_create_llm_client() + rubric_context = ( + f"{prompt_start}\n\n# Agent Trajectory Info\n## Action History\n{action_history}" + f"\n\n## Agent Output\n{final_message if final_message is not None else 'No output provided'}" + f"\n\n## Error Message\n{err_message if err_message is not None else 'No error message'}." + ) + traj = current_trajectory.get() + async with profile_span( + "oolong_llm_judge", + metadata={ + "task_id": self._task.id, + "trajectory_id": traj.id if traj is not None else None, + "parent_trajectory_id": ( + traj.parent_info.id if traj is not None and traj.parent_info is not None else None + ), + "action_history_len": len(action_history), + "final_message_len": len(final_message or ""), + "error_message_len": len(err_message or ""), + "rubric_context_len": len(rubric_context), + }, + ): + rubric_response = await rubric_client.asystem_completion( + system_prompt=rubric_system_prompt, + user_prompt=rubric_context, + temperature=1 + ) + #rubric_response = "{\"reason\": \"The agent successfully completed the task.\", \"success\": true}" + + score, reason = self.parse_rubric_response(rubric_response) reward_misc["reason"] = reason - reward_misc["rubric_dict"] = rubric_checklist.to_dict() - + #reward_misc["rubric_dict"] = rubric_checklist.to_dict() except Exception as e: reward_misc["reason"] = f"Failed rubric-based evaluation: {e}" score = 0. else: try: eval_fn = dnd_process_response if 'real' in self._task.id else synth_process_response - eval_result = eval_fn(self._task.misc, finish_message.get()) + eval_result = eval_fn(self._task.misc, str(finish_message.get())) score = eval_result['score'] reward_misc['reason'] = "Oolong environment evaluation result." reward_misc['parse_confidence'] = eval_result['parse_confidence'] @@ -166,19 +279,26 @@ def __init__(self, task: Task, per_step_subagent_success_reward: float = 0.0, per_step_subagent_reward_ceiling: float = float('inf') ): - super().__init__(task) - self._code_executor = OolongRecursiveCodeExecutor( + code_executor = OolongRecursiveCodeExecutor( task, subagent_max_steps=subagent_max_steps ) + super().__init__(task) + self._code_executor = code_executor self.subagent_max_steps = subagent_max_steps self._per_step_subagent_success_reward = per_step_subagent_success_reward self._per_step_subagent_reward_ceiling = per_step_subagent_reward_ceiling + + def _get_subagent_stats_and_reset(self) -> tuple[int, int]: + """Get (launched_count, success_count) for current step and reset for next step.""" + stats = self._code_executor.get_subagent_stats() + self._code_executor.reset_subagent_stats() + return stats async def evaluate(self) -> tuple[float, dict]: score, reward_misc = await super().evaluate() - launched, succeeded = self._code_executor.get_subagent_stats() + launched, succeeded = self._get_subagent_stats_and_reset() subagent_reward = 0.0 if self._per_step_subagent_success_reward > 0 and succeeded > 0: subagent_reward = min( diff --git a/plugins/oolong/platoon/oolong/inference_scripts/run_inference.py b/plugins/oolong/platoon/oolong/inference_scripts/run_inference.py index 9bd13bc..fd7cbfe 100644 --- a/plugins/oolong/platoon/oolong/inference_scripts/run_inference.py +++ b/plugins/oolong/platoon/oolong/inference_scripts/run_inference.py @@ -41,7 +41,7 @@ @dataclass class OolongInferenceConfig: inference: InferenceBenchmarkConfig - oolong_dataset: Literal["synth", "real"] = "synth" + oolong_dataset: Literal["synth", "real", "both"] = "synth" dataset_split: Literal["validation", "test"] = "validation" num_tasks: int = 100 use_recursive_agent: bool = False @@ -49,6 +49,8 @@ class OolongInferenceConfig: # Optional filters task_group: str | None = None # counting, user, timeline answer_type: str | None = None # NUMERIC, LABEL, COMPARISON, USER, MONTH_YEAR + min_context_len: int | None = None + max_context_len: int | None = None # full: run rollouts + report # rollouts: only collect rollouts # report: only build report from existing rollouts @@ -83,6 +85,10 @@ def get_dataset_task_ids(config: OolongInferenceConfig) -> list[str]: filter_kwargs["task_group"] = TaskGroup(config.task_group) if config.answer_type is not None: filter_kwargs["answer_type"] = AnswerType(config.answer_type) + if config.min_context_len is not None: + filter_kwargs["min_context_len"] = config.min_context_len + if config.max_context_len is not None: + filter_kwargs["max_context_len"] = config.max_context_len task_ids = get_task_ids( dataset=config.oolong_dataset, @@ -99,6 +105,14 @@ def get_dataset_task_ids(config: OolongInferenceConfig) -> list[str]: if config.num_tasks is not None and config.num_tasks > 0: task_ids = task_ids[: config.num_tasks] + if not task_ids: + raise ValueError( + "No Oolong tasks matched the requested filters: " + f"dataset={config.oolong_dataset}, split={config.dataset_split}, " + f"task_group={config.task_group}, answer_type={config.answer_type}, " + f"min_context_len={config.min_context_len}, max_context_len={config.max_context_len}." + ) + return task_ids diff --git a/plugins/oolong/platoon/oolong/rollout.py b/plugins/oolong/platoon/oolong/rollout.py index 793b690..eade5da 100644 --- a/plugins/oolong/platoon/oolong/rollout.py +++ b/plugins/oolong/platoon/oolong/rollout.py @@ -121,7 +121,7 @@ async def run_recursive_rollout(task: Task, config: RolloutConfig) -> dict | Tra traj_collection = TrajectoryCollection() current_trajectory_collection.set(traj_collection) - budget_tracker.set(DepthAwareStepBudgetTracker(max_depth=12)) + budget_tracker.set(DepthAwareStepBudgetTracker(max_depth=2)) events_path = os.path.join( config.output_dir, diff --git a/plugins/oolong/platoon/oolong/tasks.py b/plugins/oolong/platoon/oolong/tasks.py index 45260b7..a2c283e 100644 --- a/plugins/oolong/platoon/oolong/tasks.py +++ b/plugins/oolong/platoon/oolong/tasks.py @@ -31,6 +31,65 @@ class AnswerType(str, Enum): _REAL_VALIDATION_DATA: Optional[List[Dict]] = None _REAL_TEST_DATA: Optional[List[Dict]] = None _TASKS: Dict[str, Task] = {} +_VALID_DATASETS = ("synth", "real") + + +def _normalize_datasets( + dataset: Literal["synth", "real", "both"] | list[str], +) -> list[str]: + """Normalize dataset selection into a deduplicated ordered list.""" + if isinstance(dataset, str): + datasets = list(_VALID_DATASETS) if dataset == "both" else [dataset] + else: + datasets = list(dataset) + + invalid = [ds for ds in datasets if ds not in _VALID_DATASETS] + if invalid: + raise ValueError( + f"Invalid dataset(s): {invalid}. Must be drawn from {_VALID_DATASETS}." + ) + + normalized: list[str] = [] + for ds in datasets: + if ds not in normalized: + normalized.append(ds) + return normalized + + +def _get_example_context_len(example: Dict) -> Optional[int]: + """Get context length directly from the raw context text.""" + context_text = example.get("context_window_text") or example.get("context") + if context_text is None: + return None + + return len(context_text) + + +def _matches_filters( + example: Dict, + task_group: Optional[TaskGroup] = None, + answer_type: Optional[AnswerType] = None, + min_context_len: Optional[int] = None, + max_context_len: Optional[int] = None, +) -> bool: + """Return whether a dataset example matches all requested filters.""" + if task_group and example.get("task_group") != task_group.value: + return False + if answer_type and example.get("answer_type") != answer_type.value: + return False + + if min_context_len is None and max_context_len is None: + return True + + context_len = _get_example_context_len(example) + if context_len is None: + return False + if min_context_len is not None and context_len < min_context_len: + return False + if max_context_len is not None and context_len > max_context_len: + return False + + return True def _load_dataset_from_hf(dataset_name: str, split: str) -> List[Dict]: @@ -49,9 +108,11 @@ def _load_dataset_from_hf(dataset_name: str, split: str) -> List[Dict]: raise ImportError( "datasets library is required. Install with: pip install datasets" ) - hf_name = f"oolongbench/{dataset_name}" - dataset = load_dataset(hf_name, split=split) + if dataset_name == "oolong-real": + dataset = load_dataset(hf_name, "dnd", split=split) + else: + dataset = load_dataset(hf_name, split=split) return [dict(example) for example in dataset] @@ -104,6 +165,7 @@ def _example_to_task(example: Dict, dataset: str, split: str, idx: int) -> Task: # Get context text example['context'] = example.pop("context_window_text") + example.pop('context_window_text_with_labels', None) return Task( goal=example['question'], @@ -117,6 +179,7 @@ def get_synth_task_ids( split: Literal["validation", "test"] = "validation", task_group: Optional[TaskGroup] = None, answer_type: Optional[AnswerType] = None, + min_context_len: Optional[int] = None, max_context_len: Optional[int] = None, ) -> List[str]: """Get task IDs for oolong-synth dataset. @@ -125,6 +188,7 @@ def get_synth_task_ids( split: "validation" or "test" task_group: Filter by task group (counting, user, timeline) answer_type: Filter by answer type (NUMERIC, LABEL, etc.) + min_context_len: Filter to only include tasks with context >= this length max_context_len: Filter to only include tasks with context <= this length Returns: @@ -134,12 +198,13 @@ def get_synth_task_ids( task_ids = [] for idx, example in enumerate(data): - # Apply filters - if task_group and example.get("task_group") != task_group.value: - continue - if answer_type and example.get("answer_type") != answer_type.value: - continue - if max_context_len and example.get("context_len", 0) > max_context_len: + if not _matches_filters( + example, + task_group=task_group, + answer_type=answer_type, + min_context_len=min_context_len, + max_context_len=max_context_len, + ): continue task_ids.append(f"oolong.synth.{split}.{idx}") @@ -151,6 +216,7 @@ def get_real_task_ids( split: Literal["validation", "test"] = "validation", task_group: Optional[TaskGroup] = None, answer_type: Optional[AnswerType] = None, + min_context_len: Optional[int] = None, max_context_len: Optional[int] = None, ) -> List[str]: """Get task IDs for oolong-real dataset. @@ -159,6 +225,7 @@ def get_real_task_ids( split: "validation" or "test" task_group: Filter by task group answer_type: Filter by answer type + min_context_len: Filter to only include tasks with context >= this length max_context_len: Filter to only include tasks with context <= this length Returns: @@ -168,12 +235,13 @@ def get_real_task_ids( task_ids = [] for idx, example in enumerate(data): - # Apply filters - if task_group and example.get("task_group") != task_group.value: - continue - if answer_type and example.get("answer_type") != answer_type.value: - continue - if max_context_len and example.get("context_len", 0) > max_context_len: + if not _matches_filters( + example, + task_group=task_group, + answer_type=answer_type, + min_context_len=min_context_len, + max_context_len=max_context_len, + ): continue task_ids.append(f"oolong.real.{split}.{idx}") @@ -182,26 +250,27 @@ def get_real_task_ids( def get_task_ids( - dataset: Literal["synth", "real"] = "synth", + dataset: Literal["synth", "real", "both"] | list[str] = "synth", split: Literal["validation", "test"] = "validation", **kwargs ) -> List[str]: """Get task IDs for oolong benchmark. Args: - dataset: "synth" or "real" + dataset: "synth", "real", "both", or a list containing dataset names split: "validation" or "test" - **kwargs: Additional filters (task_group, answer_type, max_context_len) + **kwargs: Additional filters (task_group, answer_type, min_context_len, max_context_len) Returns: List of task IDs """ - if dataset == "synth": - return get_synth_task_ids(split, **kwargs) - elif dataset == "real": - return get_real_task_ids(split, **kwargs) - else: - raise ValueError(f"Invalid dataset: {dataset}. Must be 'synth' or 'real'") + task_ids: list[str] = [] + for dataset_name in _normalize_datasets(dataset): + if dataset_name == "synth": + task_ids.extend(get_synth_task_ids(split, **kwargs)) + elif dataset_name == "real": + task_ids.extend(get_real_task_ids(split, **kwargs)) + return task_ids def load_task_from_hf(task_id: str) -> Task: @@ -253,14 +322,14 @@ def get_task(task_id: str) -> Task: def create_oolong_datasets( - dataset: Literal["synth", "real"] = "synth", + dataset: Literal["synth", "real", "both"] | list[str] = "synth", max_samples: Optional[int] = None, **kwargs ) -> tuple[List[Task], List[Task]]: """Create validation and test datasets for Oolong. Args: - dataset: "synth" or "real" + dataset: "synth", "real", "both", or a list containing dataset names max_samples: Maximum number of samples per split (None for all) **kwargs: Additional filters diff --git a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py index 26a8db1..8171968 100644 --- a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py +++ b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py @@ -4,28 +4,29 @@ python -m platoon.oolong.train_areal --config oolong_areal.yaml """ -from dataclasses import dataclass -from copy import deepcopy import random import sys -import logging +from copy import deepcopy +from dataclasses import dataclass + from datasets import Dataset from areal.api.cli_args import load_expr_config -# Enable debug logging for platoon workflows -logging.basicConfig(level=logging.WARNING) # Quiet by default -logging.getLogger("platoon.train.areal.workflows").setLevel(logging.DEBUG) -logging.getLogger("httpx").setLevel(logging.WARNING) # Silence httpx spam - -from platoon.oolong.tasks import get_task_ids, get_task +from platoon.oolong.tasks import AnswerType, TaskGroup, get_task, get_task_ids from platoon.oolong.rollout import run_rollout, run_recursive_rollout from platoon.train.areal import PlatoonArealRLTrainer, PlatoonArealRLTrainerConfig from platoon.train.areal.workflows import StepWiseArealWorkflow + @dataclass class OolongArealTrainerConfig(PlatoonArealRLTrainerConfig): recursive: bool = False seed: int = 42 + oolong_dataset: str = "synth" + task_group: str | None = None + answer_type: str | None = None + min_context_len: int | None = None + max_context_len: int | None = None def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: """Extract Oolong reward components from trajectory steps.""" @@ -42,15 +43,38 @@ def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: return score, rewards_dict +def get_filtered_task_ids(config: OolongArealTrainerConfig, split: str) -> list[str]: + """Get task IDs for a split using the configured Oolong dataset filters.""" + filter_kwargs = {} + if config.task_group is not None: + filter_kwargs["task_group"] = TaskGroup(config.task_group) + if config.answer_type is not None: + filter_kwargs["answer_type"] = AnswerType(config.answer_type) + if config.min_context_len is not None: + filter_kwargs["min_context_len"] = config.min_context_len + if config.max_context_len is not None: + filter_kwargs["max_context_len"] = config.max_context_len + + task_ids = get_task_ids(config.oolong_dataset, split, **filter_kwargs) + if not task_ids: + raise ValueError( + "No Oolong tasks matched the requested filters: " + f"dataset={config.oolong_dataset}, split={split}, " + f"task_group={config.task_group}, answer_type={config.answer_type}, " + f"min_context_len={config.min_context_len}, max_context_len={config.max_context_len}." + ) + return task_ids + + def main(args): config, _ = load_expr_config(args, OolongArealTrainerConfig) config: OolongArealTrainerConfig = config - # Create datasets from oolong-synth + # Create datasets from Oolong. There is no train split, so validation is used + # for training and test is used for evaluation. # Note: oolong has validation and test splits, no train split - # We use validation for training and test for evaluation - train_task_ids = get_task_ids("synth", "validation") - eval_task_ids = get_task_ids("synth", "test") + train_task_ids = get_filtered_task_ids(config, "validation") + eval_task_ids = get_filtered_task_ids(config, "test") # Shuffle before truncating to get diverse samples (use seed from config) random.seed(config.seed) diff --git a/plugins/oolong/pyproject.toml b/plugins/oolong/pyproject.toml index 8aab100..58bd396 100644 --- a/plugins/oolong/pyproject.toml +++ b/plugins/oolong/pyproject.toml @@ -43,6 +43,7 @@ override-dependencies = [ "huggingface_hub==0.34", "datasets==4.3.0", "networkx==3.3.0", # This can be removed if ai-rubric pins 3.3.0 or areal relaxes the pin. + "transformers==4.57.3", ] no-build-isolation-package = ['flash-attn'] diff --git a/plugins/oolong/uv.lock b/plugins/oolong/uv.lock index 65c8cde..35da5a2 100644 --- a/plugins/oolong/uv.lock +++ b/plugins/oolong/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.12.*" resolution-markers = [ "sys_platform == 'win32' and extra != 'extra-14-platoon-oolong-areal' and extra == 'extra-14-platoon-oolong-tinker'", @@ -31,6 +31,7 @@ overrides = [ { name = "openai", specifier = "==1.99.6" }, { name = "outlines-core", specifier = "==0.1.26" }, { name = "pyarrow", specifier = "==20.0.0" }, + { name = "transformers", specifier = "==4.57.3" }, { name = "xgrammar", specifier = "==0.1.24" }, ] @@ -54,7 +55,7 @@ wheels = [ [[package]] name = "ai-rubric" -version = "0.2.2" +version = "0.2.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jinja2" }, @@ -62,9 +63,9 @@ dependencies = [ { name = "networkx" }, { name = "plotly" }, ] -sdist = { url = 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= "typing-extensions" }, ] diff --git a/uv.lock b/uv.lock index 6dac3ba..043e8c6 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.12.*" resolution-markers = [ "sys_platform == 'linux' and extra != 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker'", @@ -48,7 +48,7 @@ wheels = [ [[package]] name = "ai-rubric" -version = "0.2.2" +version = "0.2.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jinja2" }, @@ -56,9 +56,9 @@ dependencies = [ { name = "networkx" }, { name = "plotly" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/0e/e9/d131e3932fd01ec0a820990b983c31860480d47093078a093170aa9dfdbb/ai_rubric-0.2.2.tar.gz", hash = "sha256:ce8aaa222e84ee6495d37b3dfb94174b257f08c3e5ec13b88f816dd6afbcb99e", size = 694896, upload-time = "2025-09-07T04:17:36.265Z" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/3b/e8fd1ff134c396b4d6e4449f72970ccd6ddc4db01a13b11ecc5ba165e968/ai_rubric-0.2.4.tar.gz", hash = "sha256:2ccf2db3d87a2cc234e0dfa8cbcac45aabb79f359d30c648b0900e84792e9e60", size = 731238, upload-time = "2026-03-09T23:59:07.895Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/cd/dc/9d6b7bd939facd39035aea07b28ebf3510d89a5ea9f25b00f9a18a56d6fd/ai_rubric-0.2.2-py3-none-any.whl", hash = "sha256:55f3c8916b6468cca3dd7fde6f5e8c6525966c0e759de5f5c593acbc7d07c544", size = 38770, upload-time = "2025-09-07T04:17:35.16Z" }, + { url = "https://files.pythonhosted.org/packages/bd/4b/26ac0c1ede93b0e345debbf517d4ea8ac1b8993f33ce88583b5e8624bd8d/ai_rubric-0.2.4-py3-none-any.whl", hash = "sha256:343ec6c29fdc538aa312719eb0a76b611e21d3a633332d4f7cc1b836932946d3", size = 39554, upload-time = "2026-03-09T23:59:06.634Z" }, ] [[package]] @@ -676,7 +676,7 @@ name = "cffi" version = "2.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "pycparser", marker = "implementation_name != 'PyPy'" }, + { name = "pycparser", marker = "implementation_name != 'PyPy' or (extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } wheels = [ @@ -760,7 +760,7 @@ name = "click" version = "8.2.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = 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or platform_machine == 'arm64' or platform_machine == 'x86_64'" }, + { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, { name = "packaging" }, { name = "pyyaml" }, { name = "requests" }, @@ -1938,12 +1938,12 @@ name = "ipython" version = "9.7.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, { name = "decorator" }, { name = "ipython-pygments-lexers" }, { name = "jedi" }, { name = "matplotlib-inline" }, - { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "pexpect", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform == 'win32' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, { name = "prompt-toolkit" }, { name = "pygments" }, { name = "stack-data" }, @@ -2652,13 +2652,13 @@ name = "mlx-lm" version = "0.29.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "jinja2", marker = "sys_platform != 'linux'" }, + { name = "jinja2", marker = "sys_platform == 'darwin'" }, { name = "mlx", marker = "sys_platform == 'darwin'" }, - { name = "numpy", marker = "sys_platform != 'linux'" }, - { name = "protobuf", marker = "sys_platform != 'linux'" }, - { name = "pyyaml", marker = "sys_platform != 'linux'" }, - { name = "sentencepiece", marker = "sys_platform != 'linux'" }, - { name = "transformers", version = "4.56.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" }, + { name = "numpy", marker = "sys_platform == 'darwin'" }, + { name = "protobuf", marker = "sys_platform == 'darwin'" }, + { name = "pyyaml", marker = "sys_platform == 'darwin'" }, + { name = "sentencepiece", marker = "sys_platform == 'darwin'" }, + { name = "transformers", version = "4.56.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/e3/62/f46e1355256a114808517947f8e83ad6be310c7288c551db0fa678f47923/mlx_lm-0.29.1.tar.gz", hash = "sha256:b99180d8f33d33a077b814e550bfb2d8a59ae003d668fd1f4b3fff62a381d34b", size = 232302, upload-time = "2025-12-16T16:58:27.959Z" } wheels = [ @@ -3076,7 +3076,7 @@ name = "nvidia-cudnn-cu12" version = "9.10.2.21" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" }, + { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine == 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sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform == 'linux' and extra != 'extra-7-platoon-areal') or (sys_platform != 'linux' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, + { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform == 'linux' and extra != 'extra-7-platoon-areal') or (sys_platform != 'linux' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform == 'linux' and extra != 'extra-7-platoon-areal') or (sys_platform != 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"platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "triton", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform != 'linux' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, { name = "typing-extensions" }, ] wheels = [ @@ -5402,7 +5402,7 @@ name = "tqdm" version = "4.67.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" } wheels = [ @@ -5475,7 +5475,7 @@ name = "triton" version = "3.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "setuptools", marker = "sys_platform == 'linux'" }, + { name = "setuptools", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_machine == 'aarch64' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker') or (sys_platform == 'linux' and extra != 'extra-7-platoon-areal') or (sys_platform != 'linux' and extra == 'extra-7-platoon-areal' and extra == 'extra-7-platoon-tinker')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/d0/66/b1eb52839f563623d185f0927eb3530ee4d5ffe9d377cdaf5346b306689e/triton-3.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31c1d84a5c0ec2c0f8e8a072d7fd150cab84a9c239eaddc6706c081bfae4eb04", size = 155560068, upload-time = "2025-07-30T19:58:37.081Z" }, @@ -5864,8 +5864,8 @@ name = "xformers" version = "0.0.32.post1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy", marker = "sys_platform == 'linux'" }, - { name = "torch", marker = "sys_platform == 'linux'" }, + { name = "numpy", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, + { name = "torch", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/6f/33/3b9c4d3d5b2da453d27de891df4ad653ac5795324961aa3a5c15b0353fe6/xformers-0.0.32.post1.tar.gz", hash = "sha256:1de84a45c497c8d92326986508d81f4b0a8c6be4d3d62a29b8ad6048a6ab51e1", size = 12106196, upload-time = "2025-08-14T18:07:45.486Z" } wheels = [ From b0f6c4409a65582f3451d303d7ad8a5b6860a1e1 Mon Sep 17 00:00:00 2001 From: ApGa Date: Mon, 16 Mar 2026 05:28:40 +0000 Subject: [PATCH 2/8] feat: Add average subagent success reward for oolong --- plugins/appworld/platoon/appworld/env.py | 6 +++--- plugins/oolong/platoon/oolong/env.py | 4 ++-- plugins/oolong/platoon/oolong/tasks.py | 5 +++-- .../platoon/oolong/train_scripts/areal/train_areal.py | 10 +++++++++- 4 files changed, 17 insertions(+), 8 deletions(-) diff --git a/plugins/appworld/platoon/appworld/env.py b/plugins/appworld/platoon/appworld/env.py index bd4cfbe..17f9db4 100644 --- a/plugins/appworld/platoon/appworld/env.py +++ b/plugins/appworld/platoon/appworld/env.py @@ -5,7 +5,7 @@ from IPython.core.interactiveshell import ExecutionResult from IPython.terminal.embed import InteractiveShellEmbed from pathlib import Path -from rubric.core.checklist import RubricChecklistFast +from rubric.core.checklist import RubricChecklistFast from textwrap import dedent from traitlets.config.loader import Config import ast @@ -593,8 +593,8 @@ async def evaluate(self) -> tuple[float, dict]: ) score += subagent_reward - reward_misc["subagent_launched"] = launched - reward_misc["subagent_succeeded"] = succeeded + reward_misc["reward/subagent_launched"] = launched + reward_misc["reward/subagent_succeeded"] = succeeded reward_misc["reward/subagent_success"] = subagent_reward return score, reward_misc diff --git a/plugins/oolong/platoon/oolong/env.py b/plugins/oolong/platoon/oolong/env.py index c093387..921d72a 100644 --- a/plugins/oolong/platoon/oolong/env.py +++ b/plugins/oolong/platoon/oolong/env.py @@ -307,8 +307,8 @@ async def evaluate(self) -> tuple[float, dict]: ) score += subagent_reward - reward_misc["subagent_launched"] = launched - reward_misc["subagent_succeeded"] = succeeded + reward_misc["reward/subagent_launched"] = launched + reward_misc["reward/subagent_succeeded"] = succeeded reward_misc["reward/subagent_success"] = subagent_reward return score, reward_misc diff --git a/plugins/oolong/platoon/oolong/tasks.py b/plugins/oolong/platoon/oolong/tasks.py index a2c283e..5eeb8ec 100644 --- a/plugins/oolong/platoon/oolong/tasks.py +++ b/plugins/oolong/platoon/oolong/tasks.py @@ -1,6 +1,7 @@ """Task loading for Oolong benchmark from HuggingFace datasets.""" import argparse import json +from copy import deepcopy from dataclasses import asdict from enum import Enum from pathlib import Path @@ -314,11 +315,11 @@ def get_task(task_id: str) -> Task: """ global _TASKS if task_id in _TASKS: - return _TASKS[task_id] + return deepcopy(_TASKS[task_id]) task = load_task_from_hf(task_id) _TASKS[task_id] = task - return task + return deepcopy(task) def create_oolong_datasets( diff --git a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py index 8171968..528b6b3 100644 --- a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py +++ b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py @@ -37,7 +37,15 @@ def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: if not reward_key.startswith("reward/"): continue rewards_dict[reward_key] = rewards_dict.get(reward_key, 0.0) + float(reward_value) - score = float(sum(rewards_dict.values())) + + score = rewards_dict.get("reward/success", 0.0) + + launched = rewards_dict.get("reward/subagent_launched", 0.0) + if launched > 0: + subagent_success_rate = rewards_dict["reward/subagent_succeeded"] / launched + score += 0.4 * subagent_success_rate + rewards_dict["reward/subagent_success_rate"] = subagent_success_rate + if not rewards_dict: score = float(traj.get("reward", 0.0)) return score, rewards_dict From 952ee7d37793bb90b8c7e08c01e38b712135730a Mon Sep 17 00:00:00 2001 From: ApGa Date: Mon, 16 Mar 2026 06:59:39 +0000 Subject: [PATCH 3/8] fix: Fix subagent reward logging error for oolong in reward_processor --- .../{oolong_areal.yaml => oolong_recursive_areal.yaml} | 10 +++++----- .../platoon/oolong/train_scripts/areal/train_areal.py | 1 - 2 files changed, 5 insertions(+), 6 deletions(-) rename plugins/oolong/platoon/oolong/configs/train/areal/{oolong_areal.yaml => oolong_recursive_areal.yaml} (96%) diff --git a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml similarity index 96% rename from plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml rename to plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml index 66303da..93eaadc 100644 --- a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_areal.yaml +++ b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml @@ -1,5 +1,5 @@ experiment_name: recursive-agents -trial_name: oolong-real-qwen-4b-recursive-trial-0 +trial_name: oolong-real-qwen-4b-recursive-trial-1 seed: 343 total_train_epochs: 10 @@ -26,7 +26,7 @@ allocation_mode: sglang:d4p1t1+d4p1t1 rollout: experiment_name: ${experiment_name} trial_name: ${trial_name} - max_concurrent_rollouts: 16 + max_concurrent_rollouts: 8 queue_size: null consumer_batch_size: ${train_dataset.batch_size} max_head_offpolicyness: 3 @@ -45,7 +45,7 @@ workflow_config: rollout_config: model_name: ${actor.path} max_steps: 200 - output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-0 + output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-1 verbose: True train: true timeout: 3600 @@ -72,7 +72,7 @@ actor: gradient_checkpointing: true dtype: bfloat16 mb_spec: - max_tokens_per_mb: 40000 + max_tokens_per_mb: 70000 optimizer: type: adam lr: 3e-6 @@ -128,7 +128,7 @@ sglang: skip_tokenizer_init: false dtype: ${actor.dtype} max_running_requests: null - context_length: 40001 + context_length: 16001 mem_fraction_static: 0.8 enable_lora: ${actor.use_lora} max_lora_rank: ${actor.lora_rank} diff --git a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py index 528b6b3..06f0ade 100644 --- a/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py +++ b/plugins/oolong/platoon/oolong/train_scripts/areal/train_areal.py @@ -44,7 +44,6 @@ def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: if launched > 0: subagent_success_rate = rewards_dict["reward/subagent_succeeded"] / launched score += 0.4 * subagent_success_rate - rewards_dict["reward/subagent_success_rate"] = subagent_success_rate if not rewards_dict: score = float(traj.get("reward", 0.0)) From 2b4729dbca5774b21d56638bf2693e018edf3123 Mon Sep 17 00:00:00 2001 From: ApGa Date: Tue, 17 Mar 2026 01:01:25 +0000 Subject: [PATCH 4/8] fix: Fix crash in oolong logging --- platoon/utils/llm_client.py | 11 +++++++++-- .../configs/train/areal/oolong_recursive_areal.yaml | 8 ++++---- plugins/oolong/platoon/oolong/env.py | 6 +++--- 3 files changed, 16 insertions(+), 9 deletions(-) diff --git a/platoon/utils/llm_client.py b/platoon/utils/llm_client.py index ac788ac..e4df59f 100644 --- a/platoon/utils/llm_client.py +++ b/platoon/utils/llm_client.py @@ -2,6 +2,7 @@ import asyncio import os +import re from typing import Any, TypeAlias, TypedDict, cast import litellm @@ -28,6 +29,11 @@ class ConversationWithMetadata(TypedDict): _LITELLM_SEMAPHORE_PID: int | None = None +def _sanitize_litellm_error_message(message: str) -> str: + """Remove verbose request payloads from LiteLLM error strings.""" + return re.sub(r"Payload:\s*.*", "Payload: [omitted]", message, flags=re.DOTALL) + + def _get_litellm_semaphore(): """Create a process-local semaphore when configured via env var. @@ -421,8 +427,9 @@ async def _do_request() -> ChatCompletion: return response except Exception as e: - print(f"LiteLLMClient async_chat_completion failed: {e}") - raise RuntimeError(f"LiteLLM API call failed: {e}") from e + sanitized_error = _sanitize_litellm_error_message(str(e)) + print(f"LiteLLMClient async_chat_completion failed: {sanitized_error}") + raise RuntimeError(f"LiteLLM API call failed: {sanitized_error}") from e async def aclose(self) -> None: """Close the client connection.""" diff --git a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml index 93eaadc..2c22369 100644 --- a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml +++ b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml @@ -1,5 +1,5 @@ experiment_name: recursive-agents -trial_name: oolong-real-qwen-4b-recursive-trial-1 +trial_name: oolong-real-qwen-4b-recursive-trial-3 seed: 343 total_train_epochs: 10 @@ -44,15 +44,15 @@ gconfig: workflow_config: rollout_config: model_name: ${actor.path} - max_steps: 200 - output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-1 + max_steps: 15 + output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-3 verbose: True train: true timeout: 3600 step_timeout: 3600 inference_params: temperature: 1.0 - max_completion_tokens: 8192 + max_completion_tokens: 4096 group_size: 8 depth_level_weighting: true leave_one_out_baseline: true diff --git a/plugins/oolong/platoon/oolong/env.py b/plugins/oolong/platoon/oolong/env.py index 921d72a..cb45361 100644 --- a/plugins/oolong/platoon/oolong/env.py +++ b/plugins/oolong/platoon/oolong/env.py @@ -48,7 +48,7 @@ async def reset(self) -> OolongCodeExecutor: class OolongRecursiveCodeExecutor(OolongCodeExecutor): - def __init__(self, task: Task, subagent_max_steps: int | None = 25): + def __init__(self, task: Task, subagent_max_steps: int | None = 15): super().__init__(task) self.subagent_max_steps = subagent_max_steps self.shell.user_ns['context'] = self.context @@ -241,8 +241,8 @@ async def evaluate(self) -> tuple[float, dict]: traj.parent_info.id if traj is not None and traj.parent_info is not None else None ), "action_history_len": len(action_history), - "final_message_len": len(final_message or ""), - "error_message_len": len(err_message or ""), + "final_message_len": len(str(final_message) or ""), + "error_message_len": len(str(err_message) or ""), "rubric_context_len": len(rubric_context), }, ): From 7447e5ab860730f4f9af1cbeae9fe35e0bce462c Mon Sep 17 00:00:00 2001 From: ApGa Date: Tue, 17 Mar 2026 03:27:57 +0000 Subject: [PATCH 5/8] fix: Implement better parsing for oolong eval helper --- plugins/oolong/platoon/oolong/eval_helpers.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/plugins/oolong/platoon/oolong/eval_helpers.py b/plugins/oolong/platoon/oolong/eval_helpers.py index 15e18e1..4fcb01d 100644 --- a/plugins/oolong/platoon/oolong/eval_helpers.py +++ b/plugins/oolong/platoon/oolong/eval_helpers.py @@ -114,13 +114,16 @@ def dnd_parse_answer(answer) -> int | str | list[str]: def dnd_parse_response(answer) -> tuple[str, str]: - match = re.search(r"\\boxed\{\\text\{([^}]*)\}\}", answer) or re.search( - r"\\boxed[\{]+([^}]*)[\}]+", answer - ) + answer = answer.strip() + match = re.search(r"\\boxed\{\\text\{([^}]*)\}\}", answer) + if not match: + match = re.search(r"\\boxed\{([^}]*)\}", answer) if match: answer = match.group(1) else: - return answer, "low" + if not answer: + return answer, "low" + return dnd_parse_answer(answer), "med" return dnd_parse_answer(answer), "high" From c92a8a17d25a21ac1f58a4982a30332a32d6ebe7 Mon Sep 17 00:00:00 2001 From: ApGa Date: Tue, 17 Mar 2026 05:38:36 +0000 Subject: [PATCH 6/8] fix: Improve oolong linear prompt and support disabling dynamic batching/zero-variance filtering --- platoon/train/areal/config_defs.py | 1 + platoon/train/areal/rl.py | 48 +++++ platoon/train/areal/workflows/step_wise.py | 13 +- .../areal/appworld_ctx40000_4b-recursive.yaml | 9 +- plugins/oolong/platoon/oolong/agent.py | 9 +- .../train/areal/oolong_linear_areal.yaml | 189 ++++++++++++++++++ plugins/oolong/platoon/oolong/env.py | 6 +- 7 files changed, 263 insertions(+), 12 deletions(-) create mode 100644 plugins/oolong/platoon/oolong/configs/train/areal/oolong_linear_areal.yaml diff --git a/platoon/train/areal/config_defs.py b/platoon/train/areal/config_defs.py index 088fd10..9e8f83f 100644 --- a/platoon/train/areal/config_defs.py +++ b/platoon/train/areal/config_defs.py @@ -18,6 +18,7 @@ class WorkflowConfig: use_subprocesses: bool = False # Enable subprocess-based rollouts for isolation leave_one_out_baseline: bool = False # Use leave-one-out baseline for advantage centering depth_level_weighting: bool = False # Weight trajectories inversely by depth-level frequency + filter_zero_variance_groups: bool = True # Preserve old behavior by rejecting zero-variance groups @dataclass diff --git a/platoon/train/areal/rl.py b/platoon/train/areal/rl.py index 85f6ca0..f09f2fb 100644 --- a/platoon/train/areal/rl.py +++ b/platoon/train/areal/rl.py @@ -30,6 +30,7 @@ from platoon.train.areal.config_defs import PlatoonArealRLTrainerConfig from platoon.utils.train import ( bcast_and_split_from_rank0, + post_process_and_redistribute_tensor_container, set_expandable_segments, tensor_container_to, ) @@ -37,6 +38,31 @@ logger = logging.getLogger("Platoon AReaL RL Trainer") +def _get_local_batch_size(batch: dict[str, torch.Tensor | list]) -> int: + if "attention_mask" in batch and torch.is_tensor(batch["attention_mask"]): + return int(batch["attention_mask"].shape[0]) + if "input_ids" in batch and torch.is_tensor(batch["input_ids"]): + return int(batch["input_ids"].shape[0]) + for value in batch.values(): + if torch.is_tensor(value) and value.ndim >= 1: + return int(value.shape[0]) + raise ValueError("Unable to infer local batch size from batch contents") + + +def _index_local_batch(batch: dict[str, torch.Tensor | list], keep: torch.Tensor) -> dict[str, torch.Tensor | list]: + local_batch_size = _get_local_batch_size(batch) + filtered: dict[str, torch.Tensor | list] = {} + for key, value in batch.items(): + if torch.is_tensor(value) and value.ndim >= 1 and value.shape[0] == local_batch_size: + filtered[key] = value[keep.to(value.device)] + elif isinstance(value, list) and len(value) == local_batch_size: + keep_list = keep.cpu().tolist() + filtered[key] = [item for item, should_keep in zip(value, keep_list) if should_keep] + else: + filtered[key] = value + return filtered + + class PlatoonArealRLTrainer: """Trainer for RL with AReaL backend. @@ -248,6 +274,28 @@ def train(self, workflow: RolloutWorkflow, eval_workflow: RolloutWorkflow): dist.barrier(device_ids=[self.actor.device.index]) current_platform.synchronize() + if batch is not None and "trainable_datums" in batch: + trainable_mask = batch.pop("trainable_datums").bool() + local_trainable = torch.tensor( + [int(trainable_mask.sum().item())], + device=self.actor.device, + dtype=torch.long, + ) + global_trainable = local_trainable.clone() + dist.all_reduce(global_trainable, op=dist.ReduceOp.SUM, group=self.actor.data_parallel_group) + + min_per_step = self.actor.data_parallel_world_size + if global_trainable.item() < min_per_step: + batch = None + elif not bool(trainable_mask.all()): + batch = _index_local_batch(batch, trainable_mask) + batch = post_process_and_redistribute_tensor_container( + batch, + shuffle=self.config.rollout.shuffle_cross_task, + ensure_divisible_by=self.config.rollout.ensure_batch_divisible_by, + group=self.actor.data_parallel_group, + ) + if batch is not None: if config.actor.recompute_logprob or config.actor.use_decoupled_loss: diff --git a/platoon/train/areal/workflows/step_wise.py b/platoon/train/areal/workflows/step_wise.py index eb98992..1fd5e03 100644 --- a/platoon/train/areal/workflows/step_wise.py +++ b/platoon/train/areal/workflows/step_wise.py @@ -184,11 +184,22 @@ async def arun_episode(self, engine: InferenceEngine, data: dict) -> dict | None tracker.denominator(**{f"{key}_mask": reward_mask}) tracker.stat(**{key: value.to(self.device)}, denominator=f"{key}_mask") + if not self.config.filter_zero_variance_groups: + train_data["trainable_datums"] = torch.ones_like( + train_data["rewards"], dtype=torch.bool + ) + if train_data["rewards"].max() == train_data["rewards"].min() and len(results) > 1: + tracker.scalar(zero_variance_reward_group=1.0) print( f"[StepWiseWorkflow] All rewards same for task {data['task_id']}: {mean_unprocessed_reward.item():.2f}" ) - return None + if self.config.filter_zero_variance_groups: + return None + train_data["trainable_datums"] = torch.zeros_like( + train_data["rewards"], dtype=torch.bool + ) + return train_data return train_data diff --git a/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive.yaml b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive.yaml index 454c9b4..c8c95fd 100644 --- a/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive.yaml +++ b/plugins/appworld/platoon/appworld/configs/areal/appworld_ctx40000_4b-recursive.yaml @@ -1,5 +1,5 @@ experiment_name: recursive-agents -trial_name: appworld-ctx40000-cispo-4b-mixed-sft-recursive-trial8 +trial_name: appworld-ctx40000-cispo-4b-mixed-sftepoch2-recursive-trial0 seed: 343 total_train_epochs: 100 @@ -37,7 +37,7 @@ workflow_config: rollout_config: model_name: ${actor.path} max_steps: 40 - output_dir: /mnt/efs/tmp/areal/experiments/appworld-ctx40000-cispo-4b-mixed-sft-recursive-trial8 + output_dir: /mnt/efs/tmp/areal/experiments/appworld-ctx40000-cispo-4b-mixed-sftepoch2-recursive-trial0 verbose: True train: true timeout: 7200 @@ -48,7 +48,8 @@ workflow_config: group_size: 8 use_subprocesses: true leave_one_out_baseline: True # Use leave-one-out baseline for advantage centering - depth_level_weighting: True # Weight trajectories inversely by depth-level frequency + depth_level_weighting: False # Weight trajectories inversely by depth-level frequency + depth_level_discount_gamma: 0.5 # Alternate strategy: discount rewards by trajectory depth as gamma^d # CISPO loss function configuration (matches tinker settings) loss_fn_config: @@ -59,7 +60,7 @@ loss_fn_config: actor: experiment_name: ${experiment_name} trial_name: ${trial_name} - path: /mnt/efs/LlamaFactory/saves/appworld-sft-qwen3-4b-mix_opus_4_5_v6/checkpoint-22 #/mnt/efs/data/echoagent/llama-factory/saves/appworld-sft-qwen3-4b-recursive_opus_4_5 + path: /mnt/efs/LlamaFactory/saves/appworld-sft-qwen3-4b-mix_opus_4_5_v6/checkpoint-44 #/mnt/efs/data/echoagent/llama-factory/saves/appworld-sft-qwen3-4b-recursive_opus_4_5 init_from_scratch: false disable_dropout: true gradient_checkpointing: true diff --git a/plugins/oolong/platoon/oolong/agent.py b/plugins/oolong/platoon/oolong/agent.py index bc2d333..0d31952 100644 --- a/plugins/oolong/platoon/oolong/agent.py +++ b/plugins/oolong/platoon/oolong/agent.py @@ -23,12 +23,9 @@ def build_system_prompt(self, obs: CodeActObservation, **context) -> str: CONTEXT ANALYSIS: -- If the length on the context is very large (>160K characters), first examine/peek into the structure of the context (what format is the data in?) -- The context often contains structured data that you can programmatically parse or split: messages with timestamps, users, and content -- Use Python string operations to parse, filter, and chunk the data -- For very large contexts (i.e., >160K characters), work with chunks rather than the entire context at once -- If you are able to programmatically process chunks (e.g., using regex, list comprehensions, etc.), prefer doing this over printing out the chunk/context to inspect it. -- But if there is no easy rule-based method to analyze the chunk, then you may have to print it out to observe it. +- If the length on the context is very large (>32K characters), first examine/peek into the structure of the context (what format is the data in?) +- The context may be structured in a way that the task can be solved via programmatic parsing or matching. **Only take this approach if you are 100% sure that you can reliably solve the task this way.** +- For the majority of cases, you should print out the context with `print(context)` to observe and read it manually and answer the question by reading the context. ANSWER SUBMISSION: - You can submit your answer using the `finish` function in the format requested in the user provided goal. diff --git a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_linear_areal.yaml b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_linear_areal.yaml new file mode 100644 index 0000000..9ec4f9a --- /dev/null +++ b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_linear_areal.yaml @@ -0,0 +1,189 @@ +experiment_name: recursive-agents +trial_name: oolong-real-qwen-4b-linear-trial-2 + +seed: 343 +total_train_epochs: 10 +tokenizer_path: ${actor.path} +recursive: false +oolong_dataset: real +task_group: null +answer_type: null +min_context_len: null +max_context_len: 240000 +#enable_offload: false + + +cluster: + n_nodes: 1 + n_gpus_per_node: 8 + fileroot: /mnt/efs/tmp/areal/experiments + name_resolve: + type: nfs + nfs_record_root: /mnt/efs/tmp/areal/name_resolve + +allocation_mode: sglang:d4p1t1+d4p1t1 + +rollout: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + max_concurrent_rollouts: 16 + queue_size: null + consumer_batch_size: ${train_dataset.batch_size} + max_head_offpolicyness: 3 + enable_rollout_tracing: false + #shuffle_cross_task: true + #ensure_batch_divisible_by: 4 + +gconfig: + n_samples: 1 + min_new_tokens: 0 + max_new_tokens: 8192 + greedy: false + temperature: 1.0 + +workflow_config: + rollout_config: + model_name: ${actor.path} + max_steps: 200 + output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-linear-trial-2 + verbose: True + train: true + timeout: 3600 + step_timeout: 3600 + inference_params: + temperature: 1.0 + max_completion_tokens: 8192 + group_size: 8 + depth_level_weighting: false + leave_one_out_baseline: false + filter_zero_variance_groups: true + +# CISPO loss function configuration (matches tinker settings) +loss_fn_config: + loss_fn: cispo + clip_low_threshold: 0.0 + clip_high_threshold: 5.0 + +actor: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: Qwen/Qwen3-4B-Instruct-2507 #OpenPipe/Qwen3-14B-Instruct + init_from_scratch: false + disable_dropout: true + gradient_checkpointing: true + dtype: bfloat16 + mb_spec: + max_tokens_per_mb: 70000 + optimizer: + type: adam + lr: 3e-6 + weight_decay: 0.017 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + lr_scheduler_type: constant + gradient_clipping: 1.0 + warmup_steps_proportion: 0.001 + group_size: ${gconfig.n_samples} + eps_clip: 0.2 + eps_clip_higher: 0.26 + temperature: ${gconfig.temperature} + reward_scaling: 1.0 + reward_bias: 0.0 + kl_ctl: 0.0 + kl_estimator: k1 + ppo_n_minibatches: 1 + recompute_logprob: false #true + use_decoupled_loss: false #true + behav_imp_weight_cap: null + dynamic_sampling: false + reward_norm: + mean_level: null + std_level: null + adv_norm: + mean_level: null + std_level: null + max_new_tokens: ${gconfig.max_new_tokens} + # lora + use_lora: false + peft_type: lora + lora_rank: 32 + lora_alpha: 16 + target_modules: [all-linear] + +ref: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: ${actor.path} + init_from_scratch: false + disable_dropout: true + dtype: ${actor.dtype} + mb_spec: + max_tokens_per_mb: 4000 + optimizer: null + +# SGLang +sglang: + model_path: ${actor.path} + random_seed: ${seed} + skip_tokenizer_init: false + dtype: ${actor.dtype} + max_running_requests: null + context_length: 70001 + mem_fraction_static: 0.8 + enable_lora: ${actor.use_lora} + max_lora_rank: ${actor.lora_rank} + lora_target_modules: ${actor.target_modules} + #attention_backend: flashinfer # Uncomment for blackwell GPUs + +# datasets +train_dataset: + batch_size: 16 + type: rl + path: "" + +valid_dataset: + batch_size: 50 + type: rl + path: "" + +# Utilities +saver: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 25 + freq_secs: null + +recover: + mode: auto + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: 1 + freq_steps: 5 + freq_secs: 3600 + +evaluator: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 5 + freq_secs: null + +stats_logger: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + wandb: + mode: online + project: recursive-agents + +launcher: + inference_server_cpus_per_gpu: 32 + inference_server_mem_per_gpu: 32768 + trainer_cpus_per_gpu: 4 + trainer_mem_per_gpu: 32768 + diff --git a/plugins/oolong/platoon/oolong/env.py b/plugins/oolong/platoon/oolong/env.py index cb45361..21288e3 100644 --- a/plugins/oolong/platoon/oolong/env.py +++ b/plugins/oolong/platoon/oolong/env.py @@ -36,6 +36,7 @@ def __init__(self, task: Task): detect_while_loops=True, detect_interactive_input=True, ) + self.shell.user_ns['context'] = self.context async def describe_action_space(self) -> str: return """Available Actions (python functions): @@ -44,6 +45,8 @@ async def describe_action_space(self) -> str: """ async def reset(self) -> OolongCodeExecutor: await super().reset() + # Re-inject bindings that are lost when the shell is recreated. + self.shell.user_ns['context'] = self.context return self class OolongRecursiveCodeExecutor(OolongCodeExecutor): @@ -212,7 +215,8 @@ async def evaluate(self) -> tuple[float, dict]: rubric_system_prompt = ( "We need to judge the performance of an agent on a task. The task relates to aggregating some information from a potentially very large context.\n" - "Read the context carefully and see if the agent's answer is accurate. If the agent accidentally got the correct answer (e.g., by using heuristics that happened to work on the context), then do not mark the agent as successful." + "1. Read the context carefully and see if the agent's answer is accurate. 2. Do not mark the agent as successful unless it prints out the context and reads it manually to answer the question.\n" + "For example, if the agent uses regex or string matching/contains logic to answer the question, this is a heuristic that may not be reliable in general and thus should not be marked as successful." "Please provide a reason and success flag (boolean value) in the following format:\n" "```json\n" "{\n" From ea3be80747932b24f3f9d4f0a6fd505aef8cc92a Mon Sep 17 00:00:00 2001 From: ApGa Date: Tue, 17 Mar 2026 06:36:20 +0000 Subject: [PATCH 7/8] fix: Fix duplication in subagent tracking --- .../train/areal/oolong_recursive_areal.yaml | 4 +- plugins/oolong/platoon/oolong/env.py | 40 +++++++++++-------- 2 files changed, 25 insertions(+), 19 deletions(-) diff --git a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml index 2c22369..a70ad1d 100644 --- a/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml +++ b/plugins/oolong/platoon/oolong/configs/train/areal/oolong_recursive_areal.yaml @@ -1,5 +1,5 @@ experiment_name: recursive-agents -trial_name: oolong-real-qwen-4b-recursive-trial-3 +trial_name: oolong-real-qwen-4b-recursive-trial-4 seed: 343 total_train_epochs: 10 @@ -45,7 +45,7 @@ workflow_config: rollout_config: model_name: ${actor.path} max_steps: 15 - output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-3 + output_dir: /mnt/efs/tmp/areal/experiments/oolong-real-qwen-4b-recursive-trial-4 verbose: True train: true timeout: 3600 diff --git a/plugins/oolong/platoon/oolong/env.py b/plugins/oolong/platoon/oolong/env.py index 21288e3..7be9282 100644 --- a/plugins/oolong/platoon/oolong/env.py +++ b/plugins/oolong/platoon/oolong/env.py @@ -56,7 +56,8 @@ def __init__(self, task: Task, subagent_max_steps: int | None = 15): self.subagent_max_steps = subagent_max_steps self.shell.user_ns['context'] = self.context self.shell.user_ns['launch_subagent'] = self.launch_subagent - self._subagent_stats_this_step: tuple[int, int] = (0, 0) + self._launched_subagent_ids_this_step: set[str] = set() + self._subagent_success_by_child_this_step: dict[str, float] = {} async def describe_action_space(self) -> str: return """Available Actions (python functions): @@ -84,16 +85,17 @@ async def reset(self) -> OolongRecursiveCodeExecutor: # Re-inject bindings that are lost when the shell is recreated self.shell.user_ns['context'] = self.context self.shell.user_ns['launch_subagent'] = self.launch_subagent - self._subagent_stats_this_step = (0, 0) + self.reset_subagent_stats() return self def reset_subagent_stats(self) -> None: """Reset subagent tracking for a new step.""" - self._subagent_stats_this_step = (0, 0) + self._launched_subagent_ids_this_step.clear() + self._subagent_success_by_child_this_step.clear() - def get_subagent_stats(self) -> tuple[int, int]: - """Get (launched_count, success_count) for current step.""" - return self._subagent_stats_this_step + def get_subagent_stats(self) -> tuple[int, float]: + """Get (unique launched children, summed child success score) for current step.""" + return len(self._launched_subagent_ids_this_step), sum(self._subagent_success_by_child_this_step.values()) async def launch_subagent(self, goal: str, context: str="") -> str: @@ -112,16 +114,20 @@ async def launch_subagent(self, goal: str, context: str="") -> str: verbose=False ) - # --- subagent success tracking --- - launched, succeeded = self._subagent_stats_this_step - launched += 1 + # Count each new child trajectory once, even if multiple launch_subagent + # coroutines complete concurrently during the same parent step. for traj_id, traj in traj_collection.trajectories.items(): if traj_id not in traj_ids_before: if traj.parent_info and traj.parent_info.id == current_traj.id: + self._launched_subagent_ids_this_step.add(traj_id) + success_reward = 0.0 if traj.steps: final_step = traj.steps[-1] - succeeded += final_step.reward - self._subagent_stats_this_step = (launched, succeeded) + reward_misc = final_step.misc.get("reward_misc", {}) + # Preserve partial-credit success scores; only exclude + # any separate delegation bonus tracked under other keys. + success_reward = float(reward_misc.get("reward/success", 0.0)) + self._subagent_success_by_child_this_step[traj_id] = success_reward return result @@ -293,8 +299,8 @@ def __init__(self, task: Task, self._per_step_subagent_success_reward = per_step_subagent_success_reward self._per_step_subagent_reward_ceiling = per_step_subagent_reward_ceiling - def _get_subagent_stats_and_reset(self) -> tuple[int, int]: - """Get (launched_count, success_count) for current step and reset for next step.""" + def _get_subagent_stats_and_reset(self) -> tuple[int, float]: + """Get per-step unique launched children and summed child success score.""" stats = self._code_executor.get_subagent_stats() self._code_executor.reset_subagent_stats() return stats @@ -302,17 +308,17 @@ def _get_subagent_stats_and_reset(self) -> tuple[int, int]: async def evaluate(self) -> tuple[float, dict]: score, reward_misc = await super().evaluate() - launched, succeeded = self._get_subagent_stats_and_reset() + launched, success_total = self._get_subagent_stats_and_reset() subagent_reward = 0.0 - if self._per_step_subagent_success_reward > 0 and succeeded > 0: + if self._per_step_subagent_success_reward > 0 and success_total > 0: subagent_reward = min( - self._per_step_subagent_success_reward * succeeded, + self._per_step_subagent_success_reward * success_total, self._per_step_subagent_reward_ceiling ) score += subagent_reward reward_misc["reward/subagent_launched"] = launched - reward_misc["reward/subagent_succeeded"] = succeeded + reward_misc["reward/subagent_succeeded"] = success_total reward_misc["reward/subagent_success"] = subagent_reward return score, reward_misc From 96fe03b0de23397c34a39043a262b73b99183a61 Mon Sep 17 00:00:00 2001 From: Apurva Gandhi Date: Thu, 19 Mar 2026 01:04:17 -0400 Subject: [PATCH 8/8] feat: Add DeepDive plugin for deepresearch --- platoon/agents/actions/subagent.py | 31 +- plugins/deepdive/platoon/deepdive/__init__.py | 28 + plugins/deepdive/platoon/deepdive/agent.py | 133 + .../configs/areal/deepdive_areal.yaml | 174 + .../configs/inference/deepdive_inference.yaml | 32 + .../configs/tinker/deepdive_tinker.yaml | 76 + plugins/deepdive/platoon/deepdive/env.py | 389 + .../inference_scripts/run_inference.py | 121 + plugins/deepdive/platoon/deepdive/rollout.py | 154 + .../deepdive/platoon/deepdive/search_tools.py | 147 + plugins/deepdive/platoon/deepdive/tasks.py | 82 + .../train_scripts/areal/train_areal.py | 97 + .../train_scripts/tinker/train_tinker.py | 113 + plugins/deepdive/pyproject.toml | 65 + plugins/deepdive/uv.lock | 6242 +++++++++++++++++ plugins/oolong/platoon/oolong/env.py | 4 +- pyproject.toml | 2 +- uv.lock | 28 +- 18 files changed, 7889 insertions(+), 29 deletions(-) create mode 100644 plugins/deepdive/platoon/deepdive/__init__.py create mode 100644 plugins/deepdive/platoon/deepdive/agent.py create mode 100644 plugins/deepdive/platoon/deepdive/configs/areal/deepdive_areal.yaml create mode 100644 plugins/deepdive/platoon/deepdive/configs/inference/deepdive_inference.yaml create mode 100644 plugins/deepdive/platoon/deepdive/configs/tinker/deepdive_tinker.yaml create mode 100644 plugins/deepdive/platoon/deepdive/env.py create mode 100644 plugins/deepdive/platoon/deepdive/inference_scripts/run_inference.py create mode 100644 plugins/deepdive/platoon/deepdive/rollout.py create mode 100644 plugins/deepdive/platoon/deepdive/search_tools.py create mode 100644 plugins/deepdive/platoon/deepdive/tasks.py create mode 100644 plugins/deepdive/platoon/deepdive/train_scripts/areal/train_areal.py create mode 100644 plugins/deepdive/platoon/deepdive/train_scripts/tinker/train_tinker.py create mode 100644 plugins/deepdive/pyproject.toml create mode 100644 plugins/deepdive/uv.lock diff --git a/platoon/agents/actions/subagent.py b/platoon/agents/actions/subagent.py index 32a92de..ab3c237 100644 --- a/platoon/agents/actions/subagent.py +++ b/platoon/agents/actions/subagent.py @@ -1,5 +1,5 @@ import asyncio -from typing import cast +from typing import cast, Any from platoon.agents.base import ForkableAgent from platoon.envs.base import ForkableEnv @@ -8,7 +8,7 @@ from platoon.episode.trajectory import BudgetExceededError -async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None = None) -> str: +async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None = None, verbose: bool = True) -> Any: """Launch a subagent to solve a task. Args: @@ -16,13 +16,13 @@ async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None max_steps: The maximum number of steps the subagent can take. Returns: - Returns the answer or finish message for the goal. + Returns the result of the subagent's execution. """ # Cast is safe here: launch_subagent only works in contexts with forkable agents/envs agent = cast(ForkableAgent, current_agent.get()) env = cast(ForkableEnv, current_env.get()) task = env.task - + subtask = task.fork(goal, max_steps, task_misc=task_misc) forked_agent = await agent.fork(subtask) forked_env = await env.fork(subtask) @@ -36,15 +36,16 @@ async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None msg += " " + guidance return msg - traj = await asyncio.create_task( - run_episode( - forked_agent, - forked_env, - timeout=episode_step_timeout.get(), + try: + traj = await asyncio.create_task( + run_episode( + forked_agent, + forked_env, + timeout=episode_step_timeout.get(), + ) ) - ) - - budget_tracker.get().release_budget(max_steps + 1) + finally: + budget_tracker.get().release_budget(max_steps + 1) used_recursive = int(budget_tracker.get().used_budget_for(traj.id)) remaining_total = int(budget_tracker.get().remaining_budget()) @@ -54,4 +55,8 @@ async def launch_subagent(goal: str, max_steps: int = 15, task_misc: dict | None f"Total remaining budget for the current task is {remaining_total} steps.\n" ) - return (traj.finish_message or traj.error_message or "") + budget_message + if verbose: + return (traj.finish_message or traj.error_message or "") + budget_message + else: + return traj.finish_message or traj.error_message or "" + diff --git a/plugins/deepdive/platoon/deepdive/__init__.py b/plugins/deepdive/platoon/deepdive/__init__.py new file mode 100644 index 0000000..5e8e401 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/__init__.py @@ -0,0 +1,28 @@ +from .agent import ( + DeepDiveAgent, + DeepDivePromptBuilder, + DeepDiveRecursiveAgent, + DeepDiveRecursivePromptBuilder, +) +from .env import ( + DeepDiveCodeExecutor, + DeepDiveEnv, + DeepDiveRecursiveCodeExecutor, + DeepDiveRecursiveEnv, +) +from .tasks import DATASET_NAME, get_task, get_task_ids, load_task_from_hf + +__all__ = [ + "DATASET_NAME", + "DeepDiveAgent", + "DeepDivePromptBuilder", + "DeepDiveRecursiveAgent", + "DeepDiveRecursivePromptBuilder", + "DeepDiveCodeExecutor", + "DeepDiveEnv", + "DeepDiveRecursiveCodeExecutor", + "DeepDiveRecursiveEnv", + "get_task", + "get_task_ids", + "load_task_from_hf", +] diff --git a/plugins/deepdive/platoon/deepdive/agent.py b/plugins/deepdive/platoon/deepdive/agent.py new file mode 100644 index 0000000..d9e16e3 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/agent.py @@ -0,0 +1,133 @@ +"""DeepDive agents and prompt builders.""" + +from __future__ import annotations + +from platoon.agents.codeact import CodeActAgent, CodeActPromptBuilder, PromptMode +from platoon.envs.codeact import CodeActObservation + + +class DeepDivePromptBuilder(CodeActPromptBuilder): + """Prompt builder for DeepDive web research tasks.""" + + def build_system_prompt(self, obs: CodeActObservation, **context) -> str: + include_reasoning = context.get("include_reasoning", self.include_reasoning) + + base_instructions = """You are a deep research agent solving a factual question by searching the web. + +You have access to Python plus web-search tools. Use them deliberately: +- Start broad, then refine based on what you learn. +- Cross-check key claims across multiple sources when possible. +- Use `await view_webpage_content(url)` when snippets are insufficient or you need detailed evidence. +- Avoid dumping huge webpage bodies into the notebook unless necessary. +- Keep intermediate notes concise and use Python to organize findings. + +ANSWER SUBMISSION: +- When you are confident, call `finish(...)`. +- The final answer should directly answer the question and stay concise unless the task explicitly asks for more detail. +""" + + if include_reasoning: + return base_instructions + """ + +You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +For your current step, first briefly reason (~1-3 sentences) in tags, then output code in tags. +Your code will be executed in a Jupyter notebook and the output will be shown to you.""" + + return base_instructions + """ + +You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +Output your code in tags.""" + + +class DeepDiveAgent(CodeActAgent): + """Agent for DeepDive tasks.""" + + def __init__( + self, + prompt_mode: PromptMode = "sequence_extension", + include_reasoning: bool = True, + **kwargs, + ): + if "prompt_builder" not in kwargs: + kwargs["prompt_builder"] = DeepDivePromptBuilder( + prompt_mode=prompt_mode, + include_reasoning=include_reasoning, + ) + super().__init__( + prompt_mode=prompt_mode, + include_reasoning=include_reasoning, + **kwargs, + ) + + +class DeepDiveRecursivePromptBuilder(DeepDivePromptBuilder): + """Prompt builder for recursive DeepDive agents.""" + + def build_system_prompt(self, obs: CodeActObservation, **context) -> str: + include_reasoning = context.get("include_reasoning", self.include_reasoning) + + base_instructions = """You are a deep research agent solving a factual question by searching the web. + +You have access to Python plus web-search tools, and you can delegate subproblems to subagents. + +RESEARCH STRATEGY: +- Break the question into a small number of meaningful subquestions. +- Search broadly first, then narrow onto the most promising sources. +- Cross-check important claims across multiple sources when possible. +- Use `await view_webpage_content(url)` when search snippets are not enough. +- Use Python to store notes, compare evidence, and synthesize findings. + +DELEGATION STRATEGY: +- Use `await launch_subagent(goal)` for coherent subproblems such as source discovery, fact verification, or answering one component of a multi-hop question. +- Tell subagents exactly what to return, including format when useful. +- Subagents can run in parallel with `await asyncio.gather(...)`. +- Subagents can themselves delegate recursively. + +ANSWER SUBMISSION: +- When you are confident, call `finish(...)`. +- The final answer should directly answer the question and stay concise unless the task explicitly asks for more detail. +""" + + if include_reasoning: + return base_instructions + """ + +You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +For your current step, first briefly reason (~1-3 sentences) about your research or delegation strategy in tags, then output code in tags. +Your code will be executed in a Jupyter notebook and the output will be shown to you.""" + + return base_instructions + """ + +You can perform actions by writing Python code blocks. You will get multiple steps to complete the task. +Output your code in tags.""" + + +class DeepDiveRecursiveAgent(DeepDiveAgent): + """Recursive DeepDive agent with subagent support.""" + + def __init__( + self, + prompt_mode: PromptMode = "sequence_extension", + include_reasoning: bool = True, + **kwargs, + ): + if "prompt_builder" not in kwargs: + kwargs["prompt_builder"] = DeepDiveRecursivePromptBuilder( + prompt_mode=prompt_mode, + include_reasoning=include_reasoning, + ) + super().__init__( + prompt_mode=prompt_mode, + include_reasoning=include_reasoning, + **kwargs, + ) + + async def fork(self, task) -> DeepDiveRecursiveAgent: + return DeepDiveRecursiveAgent( + prompt_mode=self.prompt_builder.prompt_mode, + include_reasoning=self.include_reasoning, + prompt_builder=self.prompt_builder, + llm_client=self.llm_client.fork(), + inference_params=self.inference_params, + stuck_in_loop_threshold=self.stuck_in_loop_threshold, + stuck_in_loop_window=self.stuck_in_loop_window, + ) diff --git a/plugins/deepdive/platoon/deepdive/configs/areal/deepdive_areal.yaml b/plugins/deepdive/platoon/deepdive/configs/areal/deepdive_areal.yaml new file mode 100644 index 0000000..313b95f --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/configs/areal/deepdive_areal.yaml @@ -0,0 +1,174 @@ +experiment_name: deepdive +trial_name: deepdive-recursive-trial-0 + +seed: 343 +total_train_epochs: 10 +tokenizer_path: ${actor.path} +recursive: true +train_split: qa_sft +eval_split: qa_rl +train_num_tasks: null +eval_num_tasks: 100 + +cluster: + n_nodes: 1 + n_gpus_per_node: 8 + fileroot: /mnt/efs/tmp/areal/experiments + name_resolve: + type: nfs + nfs_record_root: /mnt/efs/tmp/areal/name_resolve + +allocation_mode: sglang:d4p1t1+d4p1t1 + +rollout: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + max_concurrent_rollouts: 16 + queue_size: null + consumer_batch_size: ${train_dataset.batch_size} + max_head_offpolicyness: 3 + enable_rollout_tracing: false + +gconfig: + n_samples: 1 + min_new_tokens: 0 + max_new_tokens: 8192 + greedy: false + temperature: 1.0 + +workflow_config: + rollout_config: + model_name: ${actor.path} + max_steps: 50 + output_dir: /mnt/efs/tmp/areal/experiments/deepdive-recursive-trial-0 + verbose: true + train: true + timeout: 7200 + step_timeout: 7200 + inference_params: + temperature: 1.0 + max_completion_tokens: 2048 + group_size: 4 + +loss_fn_config: + loss_fn: cispo + clip_low_threshold: 0.0 + clip_high_threshold: 5.0 + +actor: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: Qwen/Qwen3-4B-Instruct-2507 + init_from_scratch: false + disable_dropout: true + gradient_checkpointing: true + dtype: bfloat16 + mb_spec: + max_tokens_per_mb: 40000 + optimizer: + type: adam + lr: 3e-6 + weight_decay: 0.017 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + lr_scheduler_type: constant + gradient_clipping: 1.0 + warmup_steps_proportion: 0.001 + group_size: ${gconfig.n_samples} + eps_clip: 0.2 + eps_clip_higher: 0.26 + temperature: ${gconfig.temperature} + reward_scaling: 1.0 + reward_bias: 0.0 + kl_ctl: 0.0 + kl_estimator: k1 + ppo_n_minibatches: 1 + recompute_logprob: false + use_decoupled_loss: false + behav_imp_weight_cap: null + dynamic_sampling: false + reward_norm: + mean_level: null + std_level: null + adv_norm: + mean_level: null + std_level: null + max_new_tokens: ${gconfig.max_new_tokens} + use_lora: false + peft_type: lora + lora_rank: 32 + lora_alpha: 16 + target_modules: [all-linear] + +ref: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + path: ${actor.path} + init_from_scratch: false + disable_dropout: true + dtype: ${actor.dtype} + mb_spec: + max_tokens_per_mb: 4000 + optimizer: null + +sglang: + model_path: ${actor.path} + random_seed: ${seed} + skip_tokenizer_init: false + dtype: ${actor.dtype} + max_running_requests: null + context_length: 40001 + mem_fraction_static: 0.8 + enable_lora: ${actor.use_lora} + max_lora_rank: ${actor.lora_rank} + lora_target_modules: ${actor.target_modules} + +train_dataset: + batch_size: 16 + type: rl + path: "" + +valid_dataset: + batch_size: 32 + type: rl + path: "" + +saver: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 25 + freq_secs: null + +recover: + mode: auto + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: 1 + freq_steps: 5 + freq_secs: 3600 + +evaluator: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + freq_epochs: null + freq_steps: 5 + freq_secs: null + +stats_logger: + experiment_name: ${experiment_name} + trial_name: ${trial_name} + fileroot: ${cluster.fileroot} + wandb: + mode: online + project: deepdive + +launcher: + inference_server_cpus_per_gpu: 32 + inference_server_mem_per_gpu: 32768 + trainer_cpus_per_gpu: 4 + trainer_mem_per_gpu: 32768 diff --git a/plugins/deepdive/platoon/deepdive/configs/inference/deepdive_inference.yaml b/plugins/deepdive/platoon/deepdive/configs/inference/deepdive_inference.yaml new file mode 100644 index 0000000..dfd61a0 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/configs/inference/deepdive_inference.yaml @@ -0,0 +1,32 @@ +# DeepDive inference benchmark config. +inference: + model_name: litellm_proxy/azure/gpt-4.1-mini #openai/Qwen/Qwen3-4B-Instruct-2507 + model_endpoint: https://cmu.litellm.ai #http://127.0.0.1:30000/v1 + model_api_key: null + output_dir: ./inference_results/deepdive_gpt-4.1-mini_recursive + resume: true + workflow: + num_rollouts_per_task: 1 + num_concurrent_workers: 1 + success_threshold: 1.0 + fail_fast: false + use_subprocesses: false + subprocess_max_workers: null + rollout_config: + max_steps: 50 + output_dir: ./inference_results/deepdive_gpt-4.1-mini_recursive/rollouts + verbose: true + timeout: 7200 + step_timeout: 7200 + inference_params: + temperature: 1.0 + top_p: null + max_completion_tokens: 2048 + +dataset_split: qa_rl +num_tasks: 5 +use_recursive_agent: true +task_id: null +stage: full +shuffle_tasks: true +seed: 42 diff --git a/plugins/deepdive/platoon/deepdive/configs/tinker/deepdive_tinker.yaml b/plugins/deepdive/platoon/deepdive/configs/tinker/deepdive_tinker.yaml new file mode 100644 index 0000000..7884987 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/configs/tinker/deepdive_tinker.yaml @@ -0,0 +1,76 @@ +# DeepDive Tinker training configuration + +train: + model_name: Qwen/Qwen3-4B-Instruct-2507 + renderer_name: qwen3_instruct + batch_size: 16 + num_epochs: 10 + max_training_steps: 100 + num_minibatches: 1 + num_microbatches: 1 + max_staleness: 3 + lora_rank: 32 + loss_fn: cispo + loss_fn_config: + clip_low_threshold: 0.0 + clip_high_threshold: 5.0 + optimizer: + learning_rate: 3e-5 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + grad_clip_norm: 1e12 + workflow_config: + group_size: 4 + rollout_config: + max_steps: 50 + output_dir: ./rollout_results + verbose: true + timeout: 7200 + step_timeout: 7200 + inference_params: + temperature: 1.0 + max_completion_tokens: 2048 + num_concurrent_rollout_workflow_workers: 8 + +eval: + strategy: step + every: 20 + num_concurrent_rollout_workflow_workers: 8 + workflow_config: + group_size: 1 + rollout_config: + max_steps: 50 + output_dir: ./eval_results + verbose: false + timeout: 7200 + step_timeout: 7200 + inference_params: + temperature: 1.0 + max_completion_tokens: 2048 + +checkpoint: + strategy: step + every: 10 + load_checkpoint_path: null + +log_path: ./data/user_data/apurvag/platoon/runs/logs +tinker_base_url: null + +stats: + experiment_name: deepdive-platoon-tinker + trial_name: recursive-trial0 + wandb: + mode: online + project: deepdive-platoon-tinker + entity: null + tags: + - deepdive + - tinker + +recursive: true +train_split: qa_sft +eval_split: qa_rl +train_num_tasks: null +eval_num_tasks: 100 +seed: 42 diff --git a/plugins/deepdive/platoon/deepdive/env.py b/plugins/deepdive/platoon/deepdive/env.py new file mode 100644 index 0000000..6798691 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/env.py @@ -0,0 +1,389 @@ +from __future__ import annotations + +from copy import deepcopy +import json +import re +from typing import Any + +from platoon.envs.codeact import IPythonCodeExecutor, safe_asyncio, CodeActEnv +from platoon.envs.base import SubTask, Task +from platoon.agents.actions.common import finish +from platoon.agents.actions.subagent import launch_subagent +from platoon.episode.context import current_trajectory, current_trajectory_collection, error_message, finish_message +from rubric.core.checklist import RubricChecklistFast +from rubric.utils.llm_tools import create_llm_client as rubric_create_llm_client + +from .agent import DeepDivePromptBuilder +from .search_tools import search_web, view_webpage_content + +class DeepDiveCodeExecutor(IPythonCodeExecutor): + def __init__(self, task: Task): + super().__init__( + task, + actions=( + search_web, + view_webpage_content, + finish, + safe_asyncio + ), + detect_unawaited_async_calls=False + ) + + async def describe_action_space(self) -> str: + return """Available Actions (python functions): +1. async def search_web(query: str, max_results: int = 5) -> dict: + Search the web for information related to the query. + Args: + query: The query to search for. + max_results: (optional) The maximum number of results to return. Betweem 1 and 20. Defaults to 5. + + Returns: + A dictionary containing the search results in the following format: + { + "query": str, + "follow_up_questions": list[str], + "answer": str, + "images": list[str], + "results": list[dict], + "response_time": float, + "request_id": str, + } + + A single result is a dictionary with the following keys: + { + "url": str, + "title": str, + "content": str, + "score": float, + "raw_content": str | None, + } + +2. async def view_webpage_content(url: str) -> str: +View the content of a webpage. +Args: + url: The URL of the webpage to view. + +Returns: + A string containing the content of the webpage. This may be very long. + It is wise to first inspect the size of the response before deciding to print it out + as it may exceed your context window. A good rule of thumb: if the response is greater than + 32K characters, you may want to just look at the first 32K characters or take some other + reasonable approach to avoid printing out the entire response. + +3. def finish(message: str) -> str + Complete the task with your answer. **Note that this is a synchronous function and so you should not await it.** +""" + + async def reset(self) -> DeepDiveCodeExecutor: + await super().reset() + return self + + +class DeepDiveRecursiveCodeExecutor(DeepDiveCodeExecutor): + def __init__(self, task: Task, subagent_max_steps: int | None = 25): + self.subagent_max_steps = subagent_max_steps + self._subagent_stats_this_step: tuple[int, int] = (0, 0) + self._counted_child_trajectory_ids_this_step: set[str] = set() + super().__init__(task) + self.actions = ( + self.launch_subagent, + search_web, + view_webpage_content, + finish, + safe_asyncio, + ) + self.shell = self._create_shell() + + async def describe_action_space(self) -> str: + return """Available Actions (python functions): + +1. async def launch_subagent(goal: str) -> Any: + Launch a subagent to solve a subtask. + Args: + goal: The instruction for the subagent. This can be a simple or compound task. + Subagents have the ability to recursively delegate tasks to other subagents. + Also specify the format and type of the answer you expect from the subageent. + + Returns: + The answer from the subagent. Should be in the format and type specified in the goal. + + Example: + + ps5_price_range = launch_subagent("Find the price range of a PS5 across sony, bestbuy, amazon and gamestop. Return the answer as a string of the form '$$$ - $$$$'.") + switch2_price_range = launch_subagent("Find the price range of a Switch 2 across nintendo, bestbuy, amazon and gamestop. Return the answer as a string of the form '$$$ - $$$$'.") + results = await asyncio.gather(ps5_price_range, switch2_price_range, return_exceptions=True) + print(f"The price range of a PS5 is {results[0]}") + print(f"The price range of a Switch 2 is {results[1]}") + + +Note: `asyncio` is already imported. Use `await asyncio.gather(...)` to run subagents in parallel +or `await launch_subagent(goal)` for a single subagent. **Do not forget to await** the results. + +2. async def search_web(query: str, max_results: int = 5) -> dict: + Search the web for information related to the query. + Args: + query: The query to search for. + max_results: (optional) The maximum number of results to return. Betweem 1 and 20. Defaults to 5. + + Returns: + A dictionary containing the search results in the following format: + { + "query": str, + "follow_up_questions": list[str], + "answer": str, + "images": list[str], + "results": list[dict], + "response_time": float, + "request_id": str, + } + + A single result is a dictionary with the following keys: + { + "url": str, + "title": str, + "content": str, + "score": float, + "raw_content": str | None, + } + +3. async def view_webpage_content(url: str) -> str: +View the content of a webpage. +Args: + url: The URL of the webpage to view. + +Returns: + A string containing the content of the webpage. This may be very long. + It is wise to first inspect the size of the response before deciding to print it out + as it may exceed your context window. A good rule of thumb: if the response is greater than + 32K characters, you may want to just look at the first 32K characters or take some other + reasonable approach to avoid printing out the entire response. + +4. def finish(message: str) -> str + Complete the task with your answer. **Note that this is a synchronous function and so you should not await it.** +""" + + async def reset(self) -> DeepDiveRecursiveCodeExecutor: + await super().reset() + self.reset_subagent_stats() + return self + + def reset_subagent_stats(self) -> None: + self._subagent_stats_this_step = (0, 0) + self._counted_child_trajectory_ids_this_step = set() + + def get_subagent_stats(self) -> tuple[int, int]: + return self._subagent_stats_this_step + + async def launch_subagent(self, goal: str) -> Any: + traj_collection = current_trajectory_collection.get() + current_traj = current_trajectory.get() + traj_ids_before = set(traj_collection.trajectories.keys()) + + result = await launch_subagent( + goal=goal, + max_steps=self.subagent_max_steps, + task_misc=deepcopy(self.task.misc), + verbose=False, + ) + + launched, succeeded = self._subagent_stats_this_step + candidate_children: list[str] = [] + + for traj_id, traj in traj_collection.trajectories.items(): + if traj_id in traj_ids_before or traj_id in self._counted_child_trajectory_ids_this_step: + continue + if not traj.parent_info or traj.parent_info.id != current_traj.id: + continue + if traj.task is not None and traj.task.goal == goal: + candidate_children.append(traj_id) + + if not candidate_children: + for traj_id, traj in traj_collection.trajectories.items(): + if traj_id in traj_ids_before or traj_id in self._counted_child_trajectory_ids_this_step: + continue + if traj.parent_info and traj.parent_info.id == current_traj.id: + candidate_children.append(traj_id) + + if candidate_children: + child_id = candidate_children[0] + self._counted_child_trajectory_ids_this_step.add(child_id) + launched += 1 + child_traj = traj_collection.trajectories[child_id] + if child_traj.steps: + reward_misc = child_traj.steps[-1].misc.get("reward_misc", {}) + if reward_misc.get("success", False) or float(reward_misc.get("reward/success", 0.0)) >= 1.0: + succeeded += 1 + + self._subagent_stats_this_step = (launched, succeeded) + return result + + async def fork(self, task: Task) -> DeepDiveRecursiveCodeExecutor: + return DeepDiveRecursiveCodeExecutor( + task=task, + subagent_max_steps=self.subagent_max_steps + ) + +class DeepDiveEnv(CodeActEnv): + def __init__(self, task: Task): + super().__init__(task, DeepDiveCodeExecutor(task)) + + def _parse_rubric_response(self, response: str) -> dict: + """Parse the LLM response to extract structured data. + + Args: + response: Raw response from the LLM. + + Returns: + Parsed response as dictionary. + """ + try: + # Try to find JSON code block first + json_match = re.search(r"```json\s*(.*?)\s*```", response, re.DOTALL | re.IGNORECASE) + + if json_match: + json_str = json_match.group(1).strip() + else: + # Fallback: look for any ``` code block that might contain JSON + code_match = re.search(r"```\s*(.*?)\s*```", response, re.DOTALL) + if code_match: + json_str = code_match.group(1).strip() + else: + # Last resort: try the entire response as JSON + json_str = response.strip() + + # Parse the JSON response + parsed_response = json.loads(json_str) + + # Validate required fields + if not isinstance(parsed_response, dict): + raise ValueError("Response must be a JSON object") + + required_fields = ["reason", "success"] + for field in required_fields: + if field not in parsed_response: + raise ValueError(f"Missing required field: {field}") + + return parsed_response + + except json.JSONDecodeError as e: + raise ValueError(f"Could not parse JSON response: {str(e)}") from e + + def parse_rubric_response(self, response: str) -> tuple[float, str]: + rubric_dict = self._parse_rubric_response(response) + success_flag = rubric_dict['success'] + if isinstance(success_flag, bool): + score = 1.0 if success_flag else 0.0 + reason = rubric_dict['reason'] + else: + reason = "Success flag is not a boolean value" + score = 0.0 + + return score, reason + + async def evaluate(self) -> tuple[float, dict]: + score, reward_misc = 0., {} + + final_message = finish_message.get() + if final_message is None and self._state.history: + final_message = self._state.history[-1].misc.get("finish_message") + err_message = error_message.get() + if err_message is None and self._state.history: + err_message = self._state.history[-1].misc.get("error_message") + + if self._state.finished: + if isinstance(self._task, SubTask) and self._task.parent_tasks: + try: + rubric_checklist = RubricChecklistFast(self._task.goal) + prompt_builder = DeepDivePromptBuilder() + action_history = prompt_builder.build_action_history_description(await self.observe()) + + rubric_context = f"We need to judge the performance of an agent on the task.\n\n# Agent Trajectory Info\n## Action History\n{action_history}\n\n## Final Message\n{final_message}\n\n## Error Message\n{err_message}" + score, reason = await rubric_checklist.aevaluate(include_reason=True, context=rubric_context) + + reward_misc["reason"] = reason + reward_misc["rubric_dict"] = rubric_checklist.to_dict() + + except Exception as e: + reward_misc["reason"] = f"Failed rubric-based evaluation: {e}" + score = 0. + else: + try: + if not final_message: + score, reason = 0.0, "No final message provided" + else: + ground_truth = self._task.misc["ground_truth"] + rubric_client = rubric_create_llm_client() + rubric_system_prompt = ( + "We need to judge the performance of an deepresearch agent on a task. The task requires searching the web for information across various sources and synthesizing information together to answer a question.\n" + "The agent may use subagents to solve parts of the task. Do not penalize the model for relying on subagents, unless the subtasks delegated to the subagents are not meaningful or useful for the task.\n" + "You will be given the ground truth answer to the task and the agent's answer to the task.\n" + "Please provide a reason and success flag (boolean value) in the following format:\n" + "```json\n" + "{\n" + " \"reason\": \"Brief reasoning for success flag here.\",\n" + " \"success\": \n" + "}\n" + ) + + rubric_user_prompt = f"Ground truth answer: {ground_truth}\n\nAgent's answer: {final_message}" + rubric_response = await rubric_client.asystem_completion( + system_prompt=rubric_system_prompt, + user_prompt=rubric_user_prompt, + temperature=1 + ) + score, reason = self.parse_rubric_response(rubric_response) + reward_misc["reason"] = reason + except Exception as e: + reward_misc["reason"] = f"Failed to evaluate task: {e}" + + reward_misc["success"] = bool(score >= 1.0) + reward_misc["reward/success"] = score + return score, reward_misc + + +class DeepDiveRecursiveEnv(DeepDiveEnv): + def __init__( + self, + task: Task, + subagent_max_steps: int | None = 25, + per_step_subagent_success_reward: float = 0.0, + per_step_subagent_reward_ceiling: float = float("inf"), + ): + super().__init__(task) + self._code_executor = DeepDiveRecursiveCodeExecutor( + task=task, + subagent_max_steps=subagent_max_steps + ) + self.subagent_max_steps = subagent_max_steps + self._per_step_subagent_success_reward = per_step_subagent_success_reward + self._per_step_subagent_reward_ceiling = per_step_subagent_reward_ceiling + + def _get_subagent_stats_and_reset(self) -> tuple[int, int]: + stats = self._code_executor.get_subagent_stats() + self._code_executor.reset_subagent_stats() + return stats + + async def evaluate(self) -> tuple[float, dict]: + score, reward_misc = await super().evaluate() + + launched, succeeded = self._get_subagent_stats_and_reset() + subagent_reward = 0.0 + if self._per_step_subagent_success_reward > 0 and succeeded > 0: + subagent_reward = min( + self._per_step_subagent_success_reward * succeeded, + self._per_step_subagent_reward_ceiling, + ) + score += subagent_reward + + reward_misc["subagent_launched"] = launched + reward_misc["subagent_succeeded"] = succeeded + reward_misc["reward/subagent_success"] = subagent_reward + return score, reward_misc + + async def fork(self, task: Task) -> DeepDiveRecursiveEnv: + return DeepDiveRecursiveEnv( + task=task, + subagent_max_steps=self.subagent_max_steps, + per_step_subagent_success_reward=self._per_step_subagent_success_reward, + per_step_subagent_reward_ceiling=self._per_step_subagent_reward_ceiling, + ) diff --git a/plugins/deepdive/platoon/deepdive/inference_scripts/run_inference.py b/plugins/deepdive/platoon/deepdive/inference_scripts/run_inference.py new file mode 100644 index 0000000..1e1b664 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/inference_scripts/run_inference.py @@ -0,0 +1,121 @@ +"""DeepDive inference benchmark script using OpenAI-compatible endpoints.""" + +from __future__ import annotations + +import asyncio +import json +import logging +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Literal + +from platoon.inference import ( + DefaultInferenceGroupWorkflow, + InferenceBenchmarkConfig, + InferenceBenchmarkRunner, +) +from platoon.deepdive.rollout import run_recursive_rollout, run_rollout +from platoon.deepdive.tasks import get_task, get_task_ids +from platoon.utils.config import load_config + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) + +logger = logging.getLogger(__name__) + + +@dataclass +class DeepDiveInferenceConfig: + inference: InferenceBenchmarkConfig + dataset_split: Literal["qa_rl", "qa_sft"] = "qa_rl" + num_tasks: int = 100 + use_recursive_agent: bool = True + task_id: str | None = None + stage: Literal["full", "rollouts", "report"] = "full" + shuffle_tasks: bool = False + seed: int = 42 + + +def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: + rewards_dict: dict[str, float] = {} + for step in traj.get("steps", []): + reward_misc = step.get("misc", {}).get("reward_misc", {}) + for reward_key, reward_value in reward_misc.items(): + if not reward_key.startswith("reward/"): + continue + rewards_dict[reward_key] = rewards_dict.get(reward_key, 0.0) + float(reward_value) + score = float(sum(rewards_dict.values())) + if not rewards_dict: + score = float(traj.get("reward", 0.0)) + return score, rewards_dict + + +def get_dataset_task_ids(config: DeepDiveInferenceConfig) -> list[str]: + if config.task_id is not None: + return [config.task_id] + + task_ids = get_task_ids(config.dataset_split) + selected_task_ids = list(task_ids) + if config.shuffle_tasks: + import random + + rng = random.Random(config.seed) + rng.shuffle(selected_task_ids) + if config.num_tasks is not None and config.num_tasks > 0: + selected_task_ids = selected_task_ids[: config.num_tasks] + return selected_task_ids + + +async def main(args: list[str]) -> None: + default_config = Path(__file__).parent.parent / "configs" / "inference" / "deepdive_inference.yaml" + config, _ = load_config( + args=args, + config_class=DeepDiveInferenceConfig, + default_config_path=str(default_config), + ) + + rollout_fn = run_recursive_rollout if config.use_recursive_agent else run_rollout + if config.stage == "report": + dataset = [] + else: + task_ids = get_dataset_task_ids(config) + dataset = [{"task_id": task_id} for task_id in task_ids] + + workflow = DefaultInferenceGroupWorkflow( + rollout_fn=rollout_fn, + get_task_fn=get_task, + config=config.inference.workflow, + model_name=config.inference.model_name, + model_endpoint=config.inference.model_endpoint, + model_api_key=config.inference.model_api_key, + reward_processor=reward_processor, + ) + + runner = InferenceBenchmarkRunner( + workflow=workflow, + output_dir=config.inference.output_dir, + ) + result = await runner.arun( + dataset=dataset, + resume=config.inference.resume, + run_rollouts=config.stage in {"full", "rollouts"}, + generate_report=config.stage in {"full", "report"}, + ) + + if "summary" in result: + logger.info( + "Inference benchmark complete. Final report saved under: %s", + config.inference.output_dir, + ) + print(json.dumps(result["summary"], indent=2)) + else: + logger.info("Inference rollout stage complete. Output dir: %s", config.inference.output_dir) + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + asyncio.run(main(sys.argv[1:])) diff --git a/plugins/deepdive/platoon/deepdive/rollout.py b/plugins/deepdive/platoon/deepdive/rollout.py new file mode 100644 index 0000000..858e775 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/rollout.py @@ -0,0 +1,154 @@ +"""Rollout execution for DeepDive benchmark.""" + +from __future__ import annotations + +import asyncio +import os +from logging import getLogger + +from platoon.config_defs import RolloutConfig +from platoon.envs.base import Task +from platoon.episode.context import budget_tracker, current_trajectory_collection +from platoon.episode.loop import run_episode +from platoon.episode.trajectory import DepthAwareStepBudgetTracker, TrajectoryCollection +from platoon.utils.llm_client import LiteLLMClient +from platoon.visualization.event_sinks import JsonlFileSink + +from .agent import DeepDiveAgent, DeepDiveRecursiveAgent +from .env import DeepDiveEnv, DeepDiveRecursiveEnv + +logger = getLogger("platoon.deepdive.rollout") + +_DEFAULT_SUBAGENT_SUCCESS_REWARD = 0.1 +_DEFAULT_SUBAGENT_REWARD_CEILING = 0.3 + + +async def run_rollout(task: Task, config: RolloutConfig) -> dict | TrajectoryCollection: + agent = env = None + try: + llm_client = LiteLLMClient( + model=config.model_name, + base_url=config.model_endpoint, + api_key=config.model_api_key, + ) + env = DeepDiveEnv(task) + agent = DeepDiveAgent( + llm_client=llm_client, + inference_params=config.inference_params, + ) + traj_collection = TrajectoryCollection() + current_trajectory_collection.set(traj_collection) + + events_path = os.path.join( + config.output_dir, + "events", + f"events_{task.id}_{traj_collection.id}.jsonl", + ) + traj_collection.register_event_handlers( + JsonlFileSink( + events_path, + collection_id=traj_collection.id, + process_id=os.getpid(), + ) + ) + + if config.verbose: + logger.info("Process %s: Starting rollout for task %s", os.getpid(), task.id) + + rollout_task = asyncio.create_task(run_episode(agent, env, timeout=config.step_timeout)) + try: + await asyncio.wait_for(rollout_task, timeout=config.timeout) + except asyncio.TimeoutError: + if config.verbose: + logger.error("Process %s: Rollout timed out for task %s", os.getpid(), task.id) + rollout_task.cancel() + try: + await asyncio.wait_for(rollout_task, timeout=5.0) + except (asyncio.TimeoutError, asyncio.CancelledError): + logger.warning( + "Process %s: Task cancellation did not complete in 5s for %s, abandoning", + os.getpid(), + task.id, + ) + raise + + if config.return_dict: + return current_trajectory_collection.get().to_dict() + return current_trajectory_collection.get() + except Exception as e: + if config.verbose: + print(f"Error running rollout for task {task.id}: {e}") + raise + finally: + if agent is not None: + await agent.close() + if env is not None: + await env.close() + + +async def run_recursive_rollout(task: Task, config: RolloutConfig) -> dict | TrajectoryCollection: + agent = env = None + try: + llm_client = LiteLLMClient( + model=config.model_name, + base_url=config.model_endpoint, + api_key=config.model_api_key, + ) + env = DeepDiveRecursiveEnv( + task, + per_step_subagent_success_reward=_DEFAULT_SUBAGENT_SUCCESS_REWARD, + per_step_subagent_reward_ceiling=_DEFAULT_SUBAGENT_REWARD_CEILING, + ) + agent = DeepDiveRecursiveAgent( + llm_client=llm_client, + inference_params=config.inference_params, + ) + traj_collection = TrajectoryCollection() + current_trajectory_collection.set(traj_collection) + budget_tracker.set(DepthAwareStepBudgetTracker(max_depth=4)) + + events_path = os.path.join( + config.output_dir, + "events", + f"events_{task.id}_{traj_collection.id}.jsonl", + ) + traj_collection.register_event_handlers( + JsonlFileSink( + events_path, + collection_id=traj_collection.id, + process_id=os.getpid(), + ) + ) + + if config.verbose: + logger.info("Process %s: Starting recursive rollout for task %s", os.getpid(), task.id) + + rollout_task = asyncio.create_task(run_episode(agent, env, timeout=config.step_timeout)) + try: + await asyncio.wait_for(rollout_task, timeout=config.timeout) + except asyncio.TimeoutError: + if config.verbose: + logger.error("Process %s: Rollout timed out for task %s", os.getpid(), task.id) + rollout_task.cancel() + try: + await asyncio.wait_for(rollout_task, timeout=5.0) + except (asyncio.TimeoutError, asyncio.CancelledError): + logger.warning( + "Process %s: Task cancellation did not complete in 5s for %s, abandoning", + os.getpid(), + task.id, + ) + raise + + if config.return_dict: + return current_trajectory_collection.get().to_dict() + return current_trajectory_collection.get() + except Exception as e: + if config.verbose: + print(f"Error running rollout for task {task.id}: {e}") + raise + finally: + if agent is not None: + await agent.close() + if env is not None: + await env.close() diff --git a/plugins/deepdive/platoon/deepdive/search_tools.py b/plugins/deepdive/platoon/deepdive/search_tools.py new file mode 100644 index 0000000..eba021d --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/search_tools.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +import asyncio +import os +import time +from collections import deque +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager + +from tavily import AsyncTavilyClient + +TAVILY_CLIENT = AsyncTavilyClient(api_key=os.getenv("TAVILY_API_KEY")) + +_TAVILY_LIMITER: "_TavilyLimiter | None" = None +_TAVILY_LIMITER_PID: int | None = None + + +def _env_flag(name: str, default: bool = False) -> bool: + value = os.getenv(name) + if value is None: + return default + return value.strip().lower() in {"1", "true", "yes", "on"} + + +class _TavilyLimiter: + def __init__(self, max_requests_per_minute: int, max_concurrency: int): + self._max_requests_per_minute = max_requests_per_minute + self._semaphore = asyncio.Semaphore(max_concurrency) + self._timestamps: deque[float] = deque() + self._lock = asyncio.Lock() + + async def acquire(self) -> None: + await self._semaphore.acquire() + try: + while True: + async with self._lock: + now = time.monotonic() + cutoff = now - 60.0 + while self._timestamps and self._timestamps[0] <= cutoff: + self._timestamps.popleft() + + if len(self._timestamps) < self._max_requests_per_minute: + self._timestamps.append(now) + return + + wait_time = max(0.0, 60.0 - (now - self._timestamps[0])) + await asyncio.sleep(wait_time) + except Exception: + self._semaphore.release() + raise + + def release(self) -> None: + self._semaphore.release() + + +def _get_tavily_limiter() -> "_TavilyLimiter | None": + if not _env_flag("PLATOON_TAVILY_RATE_LIMIT_ENABLED", default=False): + return None + + global _TAVILY_LIMITER, _TAVILY_LIMITER_PID + + try: + max_requests_per_minute = int(os.getenv("PLATOON_TAVILY_MAX_REQUESTS_PER_MINUTE", "100")) + max_concurrency = int(os.getenv("PLATOON_TAVILY_MAX_CONCURRENCY", "1")) + except ValueError: + return None + + if max_requests_per_minute <= 0 or max_concurrency <= 0: + return None + + pid = os.getpid() + if _TAVILY_LIMITER is None or _TAVILY_LIMITER_PID != pid: + _TAVILY_LIMITER = _TavilyLimiter( + max_requests_per_minute=max_requests_per_minute, + max_concurrency=max_concurrency, + ) + _TAVILY_LIMITER_PID = pid + + return _TAVILY_LIMITER + + +@asynccontextmanager +async def _maybe_rate_limited_tavily_request() -> AsyncIterator[None]: + limiter = _get_tavily_limiter() + if limiter is None: + yield + return + + await limiter.acquire() + try: + yield + finally: + limiter.release() + + +async def search_web(query: str, max_results: int = 5) -> dict: + """Search the web for information related to the query. + Args: + query: The query to search for. + max_results: (optional) The maximum number of results to return. Betweem 1 and 20. Defaults to 5. + + Returns: + A dictionary containing the search results in the following format: + { + "query": str, + "follow_up_questions": list[str], + "answer": str, + "images": list[str], + "results": list[dict], + "response_time": float, + "request_id": str, + } + + A single result is a dictionary with the following keys: + { + "url": str, + "title": str, + "content": str, + "score": float, + "raw_content": str | None, + } + """ + async with _maybe_rate_limited_tavily_request(): + response = await TAVILY_CLIENT.search(query=query, max_results=max_results) + return response + + +async def view_webpage_content(url: str) -> str: + """View the content of a webpage. + Args: + url: The URL of the webpage to view. + + Returns: + A string containing the content of the webpage. This may be very long. + It is wise to first inspect the size of the response before deciding to print it out + as it may exceed your context window. A good rule of thumb: if the response is greater than + 32K characters, you may want to just look at the first 32K characters or take some other + reasonable approach to avoid printing out the entire response. + """ + async with _maybe_rate_limited_tavily_request(): + response = await TAVILY_CLIENT.extract(urls=url) + + results = response["results"] + if not results: + raise ValueError("Content extraction failed for the given URL.") + + return results[0]["raw_content"] diff --git a/plugins/deepdive/platoon/deepdive/tasks.py b/plugins/deepdive/platoon/deepdive/tasks.py new file mode 100644 index 0000000..6cc988d --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/tasks.py @@ -0,0 +1,82 @@ +"""Task loading for DeepDive benchmark from HuggingFace datasets.""" + +from __future__ import annotations + +from typing import Any, Literal + +from platoon.envs.base import Task + +DATASET_NAME = "zai-org/DeepDive" +DATASET_SPLITS = ("qa_rl", "qa_sft") +DEFAULT_MAX_STEPS = 50 + +_DATA_CACHE: dict[str, list[dict[str, Any]]] = {} +_TASK_CACHE: dict[str, Task] = {} + + +def _load_dataset_from_hf(split: Literal["qa_rl", "qa_sft"]) -> list[dict[str, Any]]: + try: + from datasets import load_dataset + except ImportError as exc: + raise ImportError( + "datasets library is required. Install with: pip install datasets" + ) from exc + + dataset = load_dataset(DATASET_NAME, split=split) + return [dict(example) for example in dataset] + + +def _get_split_data(split: Literal["qa_rl", "qa_sft"]) -> list[dict[str, Any]]: + if split not in _DATA_CACHE: + _DATA_CACHE[split] = _load_dataset_from_hf(split) + return _DATA_CACHE[split] + + +def _example_to_task( + example: dict[str, Any], + split: Literal["qa_rl", "qa_sft"], + idx: int, +) -> Task: + task_id = f"deepdive.{split}.{idx}" + question = str(example["question"]) + answer = str(example["answer"]) + + task_misc = dict(example) + task_misc["ground_truth"] = answer + task_misc["dataset_name"] = DATASET_NAME + task_misc["dataset_split"] = split + task_misc["dataset_index"] = idx + + return Task( + goal=question, + id=task_id, + max_steps=DEFAULT_MAX_STEPS, + misc=task_misc, + ) + + +def get_task_ids(split: Literal["qa_rl", "qa_sft"] = "qa_sft") -> list[str]: + return [f"deepdive.{split}.{idx}" for idx, _ in enumerate(_get_split_data(split))] + + +def load_task_from_hf(task_id: str) -> Task: + parts = task_id.split(".") + if len(parts) != 3 or parts[0] != "deepdive": + raise ValueError(f"Invalid task ID format: {task_id}") + + split = parts[1] + if split not in DATASET_SPLITS: + raise ValueError(f"Invalid DeepDive split: {split}") + + idx = int(parts[2]) + data = _get_split_data(split) + if idx >= len(data): + raise IndexError(f"Task index {idx} out of range for split {split}") + + return _example_to_task(data[idx], split, idx) + + +def get_task(task_id: str) -> Task: + if task_id not in _TASK_CACHE: + _TASK_CACHE[task_id] = load_task_from_hf(task_id) + return _TASK_CACHE[task_id] \ No newline at end of file diff --git a/plugins/deepdive/platoon/deepdive/train_scripts/areal/train_areal.py b/plugins/deepdive/platoon/deepdive/train_scripts/areal/train_areal.py new file mode 100644 index 0000000..68bb6c9 --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/train_scripts/areal/train_areal.py @@ -0,0 +1,97 @@ +"""DeepDive training script using the AReaL backend.""" + +from __future__ import annotations + +import random +import sys +from copy import deepcopy +from dataclasses import dataclass + +from areal.api.cli_args import load_expr_config +from datasets import Dataset + +from platoon.deepdive.rollout import run_recursive_rollout, run_rollout +from platoon.deepdive.tasks import get_task, get_task_ids +from platoon.train.areal import PlatoonArealRLTrainer, PlatoonArealRLTrainerConfig +from platoon.train.areal.workflows import StepWiseArealWorkflow + + +@dataclass +class DeepDiveArealTrainerConfig(PlatoonArealRLTrainerConfig): + recursive: bool = True + train_split: str = "qa_sft" + eval_split: str = "qa_rl" + train_num_tasks: int | None = None + eval_num_tasks: int = 100 + seed: int = 42 + + +def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: + rewards_dict: dict[str, float] = {} + for step in traj.get("steps", []): + reward_misc = step.get("misc", {}).get("reward_misc", {}) + for reward_key, reward_value in reward_misc.items(): + if reward_key.startswith("reward/"): + rewards_dict[reward_key] = rewards_dict.get(reward_key, 0.0) + float(reward_value) + score = float(sum(rewards_dict.values())) + if not rewards_dict: + score = float(traj.get("reward", 0.0)) + return score, rewards_dict + + +def _select_task_ids(split: str, limit: int | None, seed: int) -> list[str]: + task_ids = list(get_task_ids(split)) + rng = random.Random(seed) + rng.shuffle(task_ids) + if limit is not None and limit > 0: + task_ids = task_ids[:limit] + return task_ids + + +def main(args: list[str]) -> None: + config, _ = load_expr_config(args, DeepDiveArealTrainerConfig) + config: DeepDiveArealTrainerConfig = config + + rollout_fn = run_recursive_rollout if config.recursive else run_rollout + train_task_ids = _select_task_ids(config.train_split, config.train_num_tasks, config.seed) + eval_task_ids = _select_task_ids(config.eval_split, config.eval_num_tasks, config.seed + 1) + + train_dataset = Dataset.from_list([{"task_id": task_id} for task_id in train_task_ids]) + val_dataset = Dataset.from_list([{"task_id": task_id} for task_id in eval_task_ids]) + + with PlatoonArealRLTrainer( + config=config, + train_dataset=train_dataset, + val_dataset=val_dataset, + ) as trainer: + workflow = StepWiseArealWorkflow( + rollout_fn, + get_task, + config.workflow_config, + trainer.proxy_server, + "train_rollout", + trainer.actor.device, + filter_errors=True, + reward_processor=reward_processor, + ) + + eval_workflow_config = deepcopy(config.workflow_config) + eval_workflow_config.group_size = 1 + eval_workflow = StepWiseArealWorkflow( + rollout_fn, + get_task, + eval_workflow_config, + trainer.eval_proxy_server, + "eval_rollout", + trainer.actor.device, + reward_processor=reward_processor, + ) + + trainer.train( + workflow=workflow, + eval_workflow=eval_workflow, + ) + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/plugins/deepdive/platoon/deepdive/train_scripts/tinker/train_tinker.py b/plugins/deepdive/platoon/deepdive/train_scripts/tinker/train_tinker.py new file mode 100644 index 0000000..03bbe5b --- /dev/null +++ b/plugins/deepdive/platoon/deepdive/train_scripts/tinker/train_tinker.py @@ -0,0 +1,113 @@ +"""DeepDive training script using the Tinker backend.""" + +from __future__ import annotations + +import asyncio +import logging +import random +import sys +from dataclasses import dataclass +from pathlib import Path + +from datasets import Dataset + +from platoon.deepdive.rollout import run_recursive_rollout, run_rollout +from platoon.deepdive.tasks import get_task, get_task_ids +from platoon.train.tinker.config_defs import PlatoonTinkerRLTrainerConfig +from platoon.train.tinker.rl import PlatoonTinkerRLTrainer +from platoon.train.tinker.workflows import GroupRolloutWorkflow +from platoon.utils.config import load_config + +logging.basicConfig( + level=logging.WARNING, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) + +logging.getLogger("platoon").setLevel(logging.DEBUG) + + +@dataclass +class DeepDiveTinkerTrainerConfig(PlatoonTinkerRLTrainerConfig): + recursive: bool = True + train_split: str = "qa_sft" + eval_split: str = "qa_rl" + train_num_tasks: int | None = None + eval_num_tasks: int = 100 + seed: int = 42 + + +def reward_processor(traj: dict) -> tuple[float, dict[str, float]]: + rewards_dict: dict[str, float] = {} + for step in traj.get("steps", []): + reward_misc = step.get("misc", {}).get("reward_misc", {}) + for reward_key, reward_value in reward_misc.items(): + if reward_key.startswith("reward/"): + rewards_dict[reward_key] = rewards_dict.get(reward_key, 0.0) + float(reward_value) + score = float(sum(rewards_dict.values())) + if not rewards_dict: + score = float(traj.get("reward", 0.0)) + return score, rewards_dict + + +def _select_task_ids(split: str, limit: int | None, seed: int) -> list[str]: + task_ids = list(get_task_ids(split)) + rng = random.Random(seed) + rng.shuffle(task_ids) + if limit is not None and limit > 0: + task_ids = task_ids[:limit] + return task_ids + + +async def main(args: list[str]) -> None: + default_config = Path(__file__).parent.parent.parent / "configs" / "tinker" / "deepdive_tinker.yaml" + config, _ = load_config( + args=args, + config_class=DeepDiveTinkerTrainerConfig, + default_config_path=str(default_config), + ) + + rollout_fn = run_recursive_rollout if config.recursive else run_rollout + train_task_ids = _select_task_ids(config.train_split, config.train_num_tasks, config.seed) + eval_task_ids = _select_task_ids(config.eval_split, config.eval_num_tasks, config.seed + 1) + + train_dataset = Dataset.from_list([{"task_id": task_id} for task_id in train_task_ids]) + eval_dataset = Dataset.from_list([{"task_id": task_id} for task_id in eval_task_ids]) + + trainer = PlatoonTinkerRLTrainer( + config=config, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + ) + + async with trainer: + train_workflow = GroupRolloutWorkflow( + rollout_fn=rollout_fn, + get_task_fn=get_task, + config=config.train.workflow_config, + model_info=trainer.model_info, + log_path=trainer.run_log_path, + stats_scope="train", + filter_errors=True, + reward_processor=reward_processor, + ) + + eval_workflow = GroupRolloutWorkflow( + rollout_fn=rollout_fn, + get_task_fn=get_task, + config=config.eval.workflow_config, + model_info=trainer.model_info, + log_path=trainer.run_log_path, + stats_scope="eval", + filter_errors=False, + reward_processor=reward_processor, + ) + + await trainer.train( + train_workflow=train_workflow, + eval_workflow=eval_workflow, + ) + + +if __name__ == "__main__": + asyncio.run(main(sys.argv[1:])) diff --git a/plugins/deepdive/pyproject.toml b/plugins/deepdive/pyproject.toml new file mode 100644 index 0000000..737c459 --- /dev/null +++ b/plugins/deepdive/pyproject.toml @@ -0,0 +1,65 @@ +[project] +name = 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