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tree_manager.py
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import random
from datetime import datetime, timedelta
from enum import Enum
from http import HTTPStatus
from typing import Optional, Tuple
from uuid import UUID
import numpy as np
import pydantic
import sqlalchemy as sa
from loguru import logger
from oasst_backend.api.v1.utils import prepare_conversation, prepare_conversation_message_list
from oasst_backend.config import TreeManagerConfiguration, settings
from oasst_backend.models import (
Message,
MessageEmoji,
MessageReaction,
MessageTreeState,
Task,
TextLabels,
User,
UserStats,
UserStatsTimeFrame,
message_tree_state,
)
from oasst_backend.prompt_repository import PromptRepository
from oasst_backend.scheduled_tasks import hf_feature_extraction, toxicity
from oasst_backend.utils.database_utils import (
CommitMode,
async_managed_tx_method,
managed_tx_function,
managed_tx_method,
)
from oasst_backend.utils.ranking import ranked_pairs
from oasst_shared.exceptions.oasst_api_error import OasstError, OasstErrorCode
from oasst_shared.schemas import protocol as protocol_schema
from oasst_shared.utils import utcnow
from sqlalchemy.sql.functions import coalesce
from sqlmodel import Session, and_, func, not_, or_, text, update
class TaskType(Enum):
NONE = -1
RANKING = 0
LABEL_REPLY = 1
REPLY = 2
LABEL_PROMPT = 3
PROMPT = 4
class TaskRole(Enum):
ANY = 0
PROMPTER = 1
ASSISTANT = 2
class TreeStateStats(pydantic.BaseModel):
initial_prompt_review: int
growing: int
ranking: int
ready_for_scoring: int
scoring_failed: int
ready_for_export: int
aborted_low_grade: int
halted_by_moderator: int
backlog_ranking: int
prompt_lottery_waiting: int
class ActiveTreeSizeRow(pydantic.BaseModel):
message_tree_id: UUID
goal_tree_size: int
tree_size: int
awaiting_review: Optional[int]
@property
def remaining_messages(self) -> int:
return max(0, self.goal_tree_size - self.tree_size)
class Config:
orm_mode = True
class ExtendibleParentRow(pydantic.BaseModel):
parent_id: UUID
parent_role: str
depth: int
message_tree_id: UUID
active_children_count: int
class Config:
orm_mode = True
class IncompleteRankingsRow(pydantic.BaseModel):
parent_id: UUID
role: str
children_count: int
child_min_ranking_count: int
message_tree_id: UUID
class Config:
orm_mode = True
class TreeMessageCountStats(pydantic.BaseModel):
message_tree_id: UUID
state: str
depth: int
oldest: datetime
youngest: datetime
count: int
goal_tree_size: int
@property
def completed(self) -> int:
return self.count / self.goal_tree_size
class TreeManagerStats(pydantic.BaseModel):
state_counts: dict[str, int]
message_counts: list[TreeMessageCountStats]
def halt_prompts_of_disabled_users(db: Session):
_sql_halt_prompts_of_disabled_users = """
-- remove prompts of disabled & deleted users from prompt lottery
WITH cte AS (
SELECT mts.message_tree_id
FROM message_tree_state mts
JOIN message m ON mts.message_tree_id = m.id
JOIN "user" u ON m.user_id = u.id
WHERE state = :prompt_lottery_waiting_state AND (NOT u.enabled OR u.deleted)
)
UPDATE message_tree_state mts2
SET active=false, state=:halted_by_moderator_state
FROM cte
WHERE mts2.message_tree_id = cte.message_tree_id;
"""
r = db.execute(
text(_sql_halt_prompts_of_disabled_users),
{
"prompt_lottery_waiting_state": message_tree_state.State.PROMPT_LOTTERY_WAITING,
"halted_by_moderator_state": message_tree_state.State.HALTED_BY_MODERATOR,
},
)
if r.rowcount > 0:
logger.info(f"Halted {r.rowcount} prompts of disabled users.")
class TreeManager:
def __init__(
self,
db: Session,
prompt_repository: PromptRepository,
cfg: Optional[TreeManagerConfiguration] = None,
):
self.db = db
self.cfg = cfg or settings.tree_manager
self.pr = prompt_repository
def _random_task_selection(
self,
num_ranking_tasks: int,
num_replies_need_review: int,
num_prompts_need_review: int,
num_missing_prompts: int,
num_missing_replies: int,
) -> TaskType:
"""
Determines which task to hand out to human worker.
The task type is drawn with relative weight (e.g. ranking has highest priority)
depending on what is possible with the current message trees in the database.
"""
logger.debug(
f"TreeManager._random_task_selection({num_ranking_tasks=}, {num_replies_need_review=}, "
f"{num_prompts_need_review=}, {num_missing_prompts=}, {num_missing_replies=})"
)
task_type = TaskType.NONE
task_weights = [0] * 5
if num_ranking_tasks > 0:
task_weights[TaskType.RANKING.value] = 10
if num_replies_need_review > 0:
task_weights[TaskType.LABEL_REPLY.value] = 5
if num_prompts_need_review > 0:
task_weights[TaskType.LABEL_PROMPT.value] = 5
if num_missing_replies > 0:
task_weights[TaskType.REPLY.value] = 2
if num_missing_prompts > 0:
task_weights[TaskType.PROMPT.value] = 0.01
task_weights = np.array(task_weights)
weight_sum = task_weights.sum()
if weight_sum > 1e-8:
task_weights = task_weights / weight_sum
task_type = TaskType(np.random.choice(a=len(task_weights), p=task_weights))
logger.debug(f"Selected {task_type=}")
return task_type
def _determine_task_availability_internal(
self,
num_missing_prompts: int,
extendible_parents: list[ExtendibleParentRow],
prompts_need_review: list[Message],
replies_need_review: list[Message],
incomplete_rankings: list[IncompleteRankingsRow],
) -> dict[protocol_schema.TaskRequestType, int]:
task_count_by_type: dict[protocol_schema.TaskRequestType, int] = {t: 0 for t in protocol_schema.TaskRequestType}
task_count_by_type[protocol_schema.TaskRequestType.initial_prompt] = max(0, num_missing_prompts)
task_count_by_type[protocol_schema.TaskRequestType.prompter_reply] = len(
list(filter(lambda x: x.parent_role == "assistant", extendible_parents))
)
task_count_by_type[protocol_schema.TaskRequestType.assistant_reply] = len(
list(filter(lambda x: x.parent_role == "prompter", extendible_parents))
)
task_count_by_type[protocol_schema.TaskRequestType.label_initial_prompt] = len(prompts_need_review)
task_count_by_type[protocol_schema.TaskRequestType.label_assistant_reply] = len(
list(filter(lambda m: m.role == "assistant", replies_need_review))
)
task_count_by_type[protocol_schema.TaskRequestType.label_prompter_reply] = len(
list(filter(lambda m: m.role == "prompter", replies_need_review))
)
if self.cfg.rank_prompter_replies:
task_count_by_type[protocol_schema.TaskRequestType.rank_prompter_replies] = len(
list(filter(lambda r: r.role == "prompter", incomplete_rankings))
)
task_count_by_type[protocol_schema.TaskRequestType.rank_assistant_replies] = len(
list(filter(lambda r: r.role == "assistant", incomplete_rankings))
)
task_count_by_type[protocol_schema.TaskRequestType.random] = sum(
task_count_by_type[t] for t in protocol_schema.TaskRequestType if t in task_count_by_type
)
return task_count_by_type
def _prompt_lottery(self, lang: str, max_activate: int = 1) -> int:
# Under high load the DB runs into deadlocks when many trees are released
# simultaneously (happens whens the max_active_trees setting is increased).
# To reduce the chance of write conflicts during updates of rows in the
# message_tree_state table we limit the number of trees that are activated
# per _prompt_lottery() call to max_activate.
activated = 0
while True:
stats = self.tree_counts_by_state_stats(lang=lang, only_active=True)
prompt_lottery_waiting = self.query_prompt_lottery_waiting(lang=lang)
remaining_lottery_entries = max(0, self.cfg.max_prompt_lottery_waiting - prompt_lottery_waiting)
remaining_prompt_review = max(0, self.cfg.max_initial_prompt_review - stats.initial_prompt_review)
num_missing_growing = max(0, self.cfg.max_active_trees - stats.growing)
logger.info(f"_prompt_lottery {remaining_prompt_review=}, {num_missing_growing=}")
if num_missing_growing == 0 or activated >= max_activate:
return min(num_missing_growing + remaining_prompt_review, remaining_lottery_entries)
@managed_tx_function(CommitMode.COMMIT)
def activate_one(db: Session) -> int:
# select among distinct users
authors_qry = (
db.query(Message.user_id, func.coalesce(UserStats.reply_ranked_1, 0).label("reply_ranked_1"))
.select_from(MessageTreeState)
.join(Message, MessageTreeState.message_tree_id == Message.id)
.join(User, Message.user_id == User.id)
.outerjoin(
UserStats, and_(UserStats.user_id == User.id, UserStats.time_frame == UserStatsTimeFrame.month)
)
.filter(
MessageTreeState.state == message_tree_state.State.PROMPT_LOTTERY_WAITING,
Message.lang == lang,
not_(Message.deleted),
Message.review_result,
User.enabled,
not_(User.deleted),
)
.distinct(Message.user_id)
)
author_data = authors_qry.all()
if len(author_data) == 0:
logger.info(
f"No prompts for prompt lottery available ({num_missing_growing=}, trees missing for {lang=})."
)
return False
author_ids = [data["user_id"] for data in author_data]
# add one to avoid any scenario where all weights are 0
# this also means inactive users can still occasionally be selected
weights = [data["reply_ranked_1"] + 1 for data in author_data]
# first select an author
prompt_author_id: UUID = random.choices(author_ids, weights=weights)[0]
logger.info(f"Selected random prompt author {prompt_author_id} among {len(author_data)} candidates.")
# select random prompt of author
qry = (
db.query(MessageTreeState, Message)
.select_from(MessageTreeState)
.join(Message, MessageTreeState.message_tree_id == Message.id)
.filter(
MessageTreeState.state == message_tree_state.State.PROMPT_LOTTERY_WAITING,
Message.user_id == prompt_author_id,
Message.lang == lang,
not_(Message.deleted),
Message.review_result,
)
.limit(100)
)
prompt_candidates = qry.all()
if len(prompt_candidates) == 0:
logger.warning("No prompt candidates of selected author found.")
return False
winner_prompt = random.choice(prompt_candidates)
message: Message = winner_prompt.Message
logger.info(f"Prompt lottery winner: {message.id=}")
mts: MessageTreeState = winner_prompt.MessageTreeState
mts.state = message_tree_state.State.GROWING
mts.active = True
db.add(mts)
if mts.won_prompt_lottery_date is None:
mts.won_prompt_lottery_date = utcnow()
logger.info(f"Tree entered '{mts.state}' state ({mts.message_tree_id=})")
return True
if not activate_one():
return min(num_missing_growing + remaining_prompt_review, remaining_lottery_entries)
activated += 1
def _auto_moderation(self, lang: str) -> None:
if not self.cfg.auto_mod_enabled:
return
bad_messages = self.query_moderation_bad_messages(lang=lang)
for m in bad_messages:
num_red_flag = m.emojis.get(protocol_schema.EmojiCode.red_flag)
if num_red_flag is not None and num_red_flag >= self.cfg.auto_mod_red_flags:
if m.parent_id is None:
logger.warning(
f"[AUTO MOD] Halting tree {m.message_tree_id}, initial prompt got too many red flags ({m.emojis})."
)
self.enter_low_grade_state(m.message_tree_id)
else:
logger.warning(f"[AUTO MOD] Deleting message {m.id=}, it received too many red flags ({m.emojis}).")
self.pr.mark_messages_deleted(m.id, recursive=True)
num_skip_reply = m.emojis.get(protocol_schema.EmojiCode.skip_reply)
if num_skip_reply is not None and num_skip_reply >= self.cfg.auto_mod_max_skip_reply:
logger.warning(
f"[AUTO MOD] Halting tree {m.message_tree_id} due to high skip-reply count of message {m.id=} ({m.emojis})."
)
self.halt_tree(m.id, halt=True)
def determine_task_availability(self, lang: str) -> dict[protocol_schema.TaskRequestType, int]:
self.pr.ensure_user_is_enabled()
if not lang:
lang = "en"
logger.warning("Task availability request without lang tag received, assuming lang='en'.")
if lang in self.cfg.init_prompt_disabled_langs_list:
num_missing_prompts = 0
else:
num_missing_prompts = self._prompt_lottery(lang=lang, max_activate=1)
self._auto_moderation(lang=lang)
extendible_parents, _ = self.query_extendible_parents(lang=lang)
prompts_need_review = self.query_prompts_need_review(lang=lang)
replies_need_review = self.query_replies_need_review(lang=lang)
incomplete_rankings = self.query_incomplete_rankings(lang=lang)
return self._determine_task_availability_internal(
num_missing_prompts=num_missing_prompts,
extendible_parents=extendible_parents,
prompts_need_review=prompts_need_review,
replies_need_review=replies_need_review,
incomplete_rankings=incomplete_rankings,
)
@staticmethod
def _get_label_descriptions(valid_labels: list[TextLabels]) -> list[protocol_schema.LabelDescription]:
return [
protocol_schema.LabelDescription(
name=l.value, widget=l.widget.value, display_text=l.display_text, help_text=l.help_text
)
for l in valid_labels
]
def next_task(
self,
desired_task_type: protocol_schema.TaskRequestType = protocol_schema.TaskRequestType.random,
lang: str = "en",
) -> Tuple[protocol_schema.Task, Optional[UUID], Optional[UUID]]:
logger.debug(f"TreeManager.next_task({desired_task_type=}, {lang=})")
self.pr.ensure_user_is_enabled()
if not lang:
lang = "en"
logger.warning("Task request without lang tag received, assuming 'en'.")
self._auto_moderation(lang=lang)
num_missing_prompts = self._prompt_lottery(lang=lang, max_activate=2)
# check user's pending tasks
recent_tasks_span = timedelta(seconds=self.cfg.recent_tasks_span_sec)
users_pending_tasks = self.pr.task_repository.fetch_pending_tasks_of_user(
self.pr.user_id,
max_age=recent_tasks_span,
limit=self.cfg.max_pending_tasks_per_user + 1,
)
num_pending_tasks = len(users_pending_tasks)
if num_pending_tasks >= self.cfg.max_pending_tasks_per_user:
logger.warning(
f"Rejecting task request. User {self.pr.user_id} has {num_pending_tasks} pending tasks. "
f"Oldest age: {utcnow()-users_pending_tasks[0].created_date}."
)
raise OasstError(
"User has too many pending tasks.",
OasstErrorCode.TASK_TOO_MANY_PENDING,
)
elif num_pending_tasks > 0:
logger.debug(
f"User {self.pr.user_id} has {num_pending_tasks} pending tasks. Oldest age: {utcnow()-users_pending_tasks[0].created_date}"
)
prompts_need_review = self.query_prompts_need_review(lang=lang)
replies_need_review = self.query_replies_need_review(lang=lang)
extendible_parents, active_tree_sizes = self.query_extendible_parents(lang=lang)
incomplete_rankings = self.query_incomplete_rankings(lang=lang)
if not self.cfg.rank_prompter_replies:
incomplete_rankings = list(filter(lambda r: r.role == "assistant", incomplete_rankings))
# determine type of task to generate
num_missing_replies = sum(x.remaining_messages for x in active_tree_sizes)
task_role = TaskRole.ANY
if desired_task_type == protocol_schema.TaskRequestType.random:
task_type = self._random_task_selection(
num_ranking_tasks=len(incomplete_rankings),
num_replies_need_review=len(replies_need_review),
num_prompts_need_review=len(prompts_need_review),
num_missing_prompts=num_missing_prompts,
num_missing_replies=num_missing_replies,
)
if task_type == TaskType.NONE:
logger.warning(f"No random tasks currently available, user: {self.pr.user_id}")
raise OasstError(
f"No tasks of type '{protocol_schema.TaskRequestType.random.value}' are currently available.",
OasstErrorCode.TASK_REQUESTED_TYPE_NOT_AVAILABLE,
HTTPStatus.SERVICE_UNAVAILABLE,
)
else:
task_count_by_type = self._determine_task_availability_internal(
num_missing_prompts=num_missing_prompts,
extendible_parents=extendible_parents,
prompts_need_review=prompts_need_review,
replies_need_review=replies_need_review,
incomplete_rankings=incomplete_rankings,
)
available_count = task_count_by_type.get(desired_task_type)
if not available_count:
logger.warning(f"No '{desired_task_type.value}' tasks currently available, user: {self.pr.user_id}")
raise OasstError(
f"No tasks of type '{desired_task_type.value}' are currently available.",
OasstErrorCode.TASK_REQUESTED_TYPE_NOT_AVAILABLE,
HTTPStatus.SERVICE_UNAVAILABLE,
)
task_type_role_map = {
protocol_schema.TaskRequestType.initial_prompt: (TaskType.PROMPT, TaskRole.ANY),
protocol_schema.TaskRequestType.prompter_reply: (TaskType.REPLY, TaskRole.PROMPTER),
protocol_schema.TaskRequestType.assistant_reply: (TaskType.REPLY, TaskRole.ASSISTANT),
protocol_schema.TaskRequestType.rank_prompter_replies: (TaskType.RANKING, TaskRole.PROMPTER),
protocol_schema.TaskRequestType.rank_assistant_replies: (TaskType.RANKING, TaskRole.ASSISTANT),
protocol_schema.TaskRequestType.label_initial_prompt: (TaskType.LABEL_PROMPT, TaskRole.ANY),
protocol_schema.TaskRequestType.label_assistant_reply: (TaskType.LABEL_REPLY, TaskRole.ASSISTANT),
protocol_schema.TaskRequestType.label_prompter_reply: (TaskType.LABEL_REPLY, TaskRole.PROMPTER),
}
task_type, task_role = task_type_role_map[desired_task_type]
message_tree_id = None
parent_message_id = None
logger.debug(f"selected {task_type=}")
match task_type:
case TaskType.RANKING:
if task_role == TaskRole.PROMPTER:
incomplete_rankings = list(filter(lambda m: m.role == "prompter", incomplete_rankings))
elif task_role == TaskRole.ASSISTANT:
incomplete_rankings = list(filter(lambda m: m.role == "assistant", incomplete_rankings))
if len(incomplete_rankings) > 0:
ranking_parent_id = random.choice(incomplete_rankings).parent_id
messages = self.pr.fetch_message_conversation(ranking_parent_id)
assert len(messages) > 0 and messages[-1].id == ranking_parent_id
ranking_parent = messages[-1]
assert not ranking_parent.deleted and ranking_parent.review_result
conversation = prepare_conversation(messages)
replies = self.pr.fetch_message_children(ranking_parent_id, review_result=True, deleted=False)
assert len(replies) > 1
random.shuffle(replies) # hand out replies in random order
reply_messages = prepare_conversation_message_list(replies)
if any(not m.synthetic for m in reply_messages):
reveal_synthetic = False
for rm in reply_messages:
rm.synthetic = None
else:
reveal_synthetic = True
replies = [p.text for p in replies]
if messages[-1].role == "assistant":
logger.info("Generating a RankPrompterRepliesTask.")
task = protocol_schema.RankPrompterRepliesTask(
conversation=conversation,
replies=replies,
reply_messages=reply_messages,
ranking_parent_id=ranking_parent.id,
message_tree_id=ranking_parent.message_tree_id,
reveal_synthetic=reveal_synthetic,
)
else:
logger.info("Generating a RankAssistantRepliesTask.")
task = protocol_schema.RankAssistantRepliesTask(
conversation=conversation,
replies=replies,
reply_messages=reply_messages,
ranking_parent_id=ranking_parent.id,
message_tree_id=ranking_parent.message_tree_id,
reveal_synthetic=reveal_synthetic,
)
parent_message_id = ranking_parent_id
message_tree_id = messages[-1].message_tree_id
case TaskType.LABEL_REPLY:
if task_role == TaskRole.PROMPTER:
replies_need_review = list(filter(lambda m: m.role == "prompter", replies_need_review))
elif task_role == TaskRole.ASSISTANT:
replies_need_review = list(filter(lambda m: m.role == "assistant", replies_need_review))
if len(replies_need_review) > 0:
random_reply_message = random.choice(replies_need_review)
messages = self.pr.fetch_message_conversation(random_reply_message)
conversation = prepare_conversation(messages)
message = messages[-1]
self.cfg.p_full_labeling_review_reply_prompter: float = 0.1
label_mode = protocol_schema.LabelTaskMode.full
label_disposition = protocol_schema.LabelTaskDisposition.quality
if message.role == "assistant":
valid_labels = self.cfg.labels_assistant_reply
if (
desired_task_type == protocol_schema.TaskRequestType.random
and random.random() > self.cfg.p_full_labeling_review_reply_assistant
):
label_mode = protocol_schema.LabelTaskMode.simple
label_disposition = protocol_schema.LabelTaskDisposition.spam
valid_labels = self.cfg.mandatory_labels_assistant_reply.copy()
if protocol_schema.TextLabel.lang_mismatch not in valid_labels:
valid_labels.append(protocol_schema.TextLabel.lang_mismatch)
if protocol_schema.TextLabel.quality not in valid_labels:
valid_labels.append(protocol_schema.TextLabel.quality)
logger.info(f"Generating a LabelAssistantReplyTask. ({label_mode=:s})")
task = protocol_schema.LabelAssistantReplyTask(
message_id=message.id,
conversation=conversation,
reply=message.text,
valid_labels=list(map(lambda x: x.value, valid_labels)),
mandatory_labels=list(map(lambda x: x.value, self.cfg.mandatory_labels_assistant_reply)),
mode=label_mode,
disposition=label_disposition,
labels=self._get_label_descriptions(valid_labels),
)
else:
valid_labels = self.cfg.labels_prompter_reply
if (
desired_task_type == protocol_schema.TaskRequestType.random
and random.random() > self.cfg.p_full_labeling_review_reply_prompter
):
label_mode = protocol_schema.LabelTaskMode.simple
label_disposition = protocol_schema.LabelTaskDisposition.spam
valid_labels = self.cfg.mandatory_labels_prompter_reply.copy()
if protocol_schema.TextLabel.lang_mismatch not in valid_labels:
valid_labels.append(protocol_schema.TextLabel.lang_mismatch)
if protocol_schema.TextLabel.quality not in valid_labels:
valid_labels.append(protocol_schema.TextLabel.quality)
logger.info(f"Generating a LabelPrompterReplyTask. ({label_mode=:s})")
task = protocol_schema.LabelPrompterReplyTask(
message_id=message.id,
conversation=conversation,
reply=message.text,
valid_labels=list(map(lambda x: x.value, valid_labels)),
mandatory_labels=list(map(lambda x: x.value, self.cfg.mandatory_labels_prompter_reply)),
mode=label_mode,
disposition=label_disposition,
labels=self._get_label_descriptions(valid_labels),
)
parent_message_id = message.id
message_tree_id = message.message_tree_id
case TaskType.REPLY:
if task_role == TaskRole.PROMPTER:
extendible_parents = list(filter(lambda x: x.parent_role == "assistant", extendible_parents))
elif task_role == TaskRole.ASSISTANT:
extendible_parents = list(filter(lambda x: x.parent_role == "prompter", extendible_parents))
# select a tree with missing replies
if len(extendible_parents) > 0:
random_parent: ExtendibleParentRow = None
if self.cfg.p_lonely_child_extension > 0 and self.cfg.lonely_children_count > 1:
# check if we have extendible prompter parents with a small number of replies
lonely_children_parents = [
p
for p in extendible_parents
if 0 < p.active_children_count < self.cfg.lonely_children_count
and p.parent_role == "prompter"
]
if len(lonely_children_parents) > 0 and random.random() < self.cfg.p_lonely_child_extension:
random_parent = random.choice(lonely_children_parents)
if random_parent is None:
random_parent = random.choice(extendible_parents)
# fetch random conversation to extend
logger.debug(f"selected {random_parent=}")
messages = self.pr.fetch_message_conversation(random_parent.parent_id)
assert all(m.review_result for m in messages) # ensure all messages have positive reviews
conversation = prepare_conversation(messages)
# generate reply task depending on last message
if messages[-1].role == "assistant":
logger.info("Generating a PrompterReplyTask.")
task = protocol_schema.PrompterReplyTask(conversation=conversation)
else:
logger.info("Generating a AssistantReplyTask.")
task = protocol_schema.AssistantReplyTask(conversation=conversation)
parent_message_id = messages[-1].id
message_tree_id = messages[-1].message_tree_id
case TaskType.LABEL_PROMPT:
assert len(prompts_need_review) > 0
message = random.choice(prompts_need_review)
message = self.pr.fetch_message(message.id) # re-fetch message including emojis
label_mode = protocol_schema.LabelTaskMode.full
label_disposition = protocol_schema.LabelTaskDisposition.quality
valid_labels = self.cfg.labels_initial_prompt
if random.random() > self.cfg.p_full_labeling_review_prompt:
valid_labels = self.cfg.mandatory_labels_initial_prompt.copy()
label_mode = protocol_schema.LabelTaskMode.simple
label_disposition = protocol_schema.LabelTaskDisposition.spam
if protocol_schema.TextLabel.lang_mismatch not in valid_labels:
valid_labels.append(protocol_schema.TextLabel.lang_mismatch)
logger.info(f"Generating a LabelInitialPromptTask ({label_mode=:s}).")
task = protocol_schema.LabelInitialPromptTask(
message_id=message.id,
prompt=message.text,
conversation=prepare_conversation([message]),
valid_labels=list(map(lambda x: x.value, valid_labels)),
mandatory_labels=list(map(lambda x: x.value, self.cfg.mandatory_labels_initial_prompt)),
mode=label_mode,
disposition=label_disposition,
labels=self._get_label_descriptions(valid_labels),
)
parent_message_id = message.id
message_tree_id = message.message_tree_id
case TaskType.PROMPT:
logger.info("Generating an InitialPromptTask.")
task = protocol_schema.InitialPromptTask(hint=None)
case _:
task = None
if task is None:
raise OasstError(
f"No task of type '{desired_task_type.value}' is currently available.",
OasstErrorCode.TASK_REQUESTED_TYPE_NOT_AVAILABLE,
HTTPStatus.SERVICE_UNAVAILABLE,
)
logger.info(f"Generated task (type={task.type}, id={task.id})")
logger.debug(f"Generated {task=}.")
return task, message_tree_id, parent_message_id
@async_managed_tx_method(CommitMode.FLUSH)
async def handle_interaction(self, interaction: protocol_schema.AnyInteraction) -> protocol_schema.Task:
pr = self.pr
pr.ensure_user_is_enabled()
match type(interaction):
case protocol_schema.TextReplyToMessage:
logger.info(
f"Frontend reports text reply to message_id={interaction.message_id} by user={interaction.user}."
)
logger.debug(f"with {interaction.text=}")
# here we store the text reply in the database
message = pr.store_text_reply(
text=interaction.text,
lang=interaction.lang,
frontend_message_id=interaction.message_id,
user_frontend_message_id=interaction.user_message_id,
)
if not message.parent_id:
logger.info(
f"TreeManager: Inserting new tree state for initial prompt {message.id=} [{message.lang}]"
)
self._insert_default_state(message.id, lang=message.lang)
if not settings.DEBUG_SKIP_EMBEDDING_COMPUTATION:
try:
hf_feature_extraction.delay(interaction.text, message.id, pr.api_client.dict())
logger.debug("Extract Embedding")
except OasstError:
logger.error(
f"Could not fetch embbeddings for text reply to {interaction.message_id=} with {interaction.text=} by {interaction.user=}."
)
if not settings.DEBUG_SKIP_TOXICITY_CALCULATION:
try:
toxicity.delay(interaction.text, message.id, pr.api_client.dict())
logger.debug("Sent Toxicity")
except OasstError:
logger.error(
f"Could not compute toxicity for text reply to {interaction.message_id=} with {interaction.text=} by {interaction.user=}."
)
case protocol_schema.MessageRating:
logger.info(
f"Frontend reports rating of message_id={interaction.message_id} by user={interaction.user}."
)
logger.debug(f"with {interaction.rating=}")
pr.store_rating(interaction)
case protocol_schema.MessageRanking:
logger.info(
f"Frontend reports ranking of message_id={interaction.message_id} by user={interaction.user}."
)
logger.debug(f"with {interaction.ranking=}")
_, task = pr.store_ranking(interaction)
self.check_condition_for_scoring_state(task.message_tree_id)
case protocol_schema.TextLabels:
logger.info(
f"Frontend reports labels of message_id={interaction.message_id} by user={interaction.user}."
)
logger.debug(f"with {interaction.labels=}")
_, task, msg = pr.store_text_labels(interaction)
# if it was a response for a task, check if we have enough reviews to calc review_result
if task and msg:
reviews = self.query_reviews_for_message(msg.id)
acceptance_score = self._calculate_acceptance(reviews)
logger.debug(
f"Message {msg.id=}, {acceptance_score=}, {len(reviews)=}, {msg.review_result=}, {msg.review_count=}"
)
if msg.parent_id is None:
if not msg.review_result and msg.review_count >= self.cfg.num_reviews_initial_prompt:
if acceptance_score > self.cfg.acceptance_threshold_initial_prompt:
msg.review_result = True
self.db.add(msg)
logger.info(
f"Initial prompt message was accepted: {msg.id=}, {acceptance_score=}, {len(reviews)=}"
)
else:
if msg.review_result is None:
msg.review_result = False
self.db.add(msg)
self.enter_low_grade_state(msg.message_tree_id)
self.check_condition_for_prompt_lottery(msg.message_tree_id)
elif msg.review_count >= self.cfg.num_reviews_reply:
if not msg.review_result and acceptance_score > self.cfg.acceptance_threshold_reply:
msg.review_result = True
self.db.add(msg)
logger.info(
f"Reply message message accepted: {msg.id=}, {acceptance_score=}, {len(reviews)=}"
)
elif msg.review_result is None: # do not overwrite existing review result
msg.review_result = False
self.db.add(msg)
self.check_condition_for_ranking_state(msg.message_tree_id)
case _:
raise OasstError("Invalid response type.", OasstErrorCode.TASK_INVALID_RESPONSE_TYPE)
return protocol_schema.TaskDone()
def _enter_state(self, mts: MessageTreeState, state: message_tree_state.State):
assert mts
is_terminal = state in message_tree_state.TERMINAL_STATES
was_active = mts.active
mts.active = not is_terminal
mts.state = state.value
self.db.add(mts)
self.db.flush
if is_terminal:
logger.info(f"Tree entered terminal '{mts.state}' state ({mts.message_tree_id=})")
root_msg = self.pr.fetch_message(message_id=mts.message_tree_id, fail_if_missing=False)
if root_msg and was_active:
if random.random() < self.cfg.p_activate_backlog_tree:
self.activate_backlog_tree(lang=root_msg.lang)
if self.cfg.min_active_rankings_per_lang > 0:
incomplete_rankings = self.query_incomplete_rankings(lang=root_msg.lang, user_filter=False)
if len(incomplete_rankings) < self.cfg.min_active_rankings_per_lang:
self.activate_backlog_tree(lang=root_msg.lang)
else:
if mts.state == message_tree_state.State.GROWING and mts.won_prompt_lottery_date is None:
mts.won_prompt_lottery_date = utcnow()
logger.info(f"Tree entered '{mts.state}' state ({mts.message_tree_id=})")
def enter_low_grade_state(self, message_tree_id: UUID) -> None:
logger.debug(f"enter_low_grade_state({message_tree_id=})")
mts = self.pr.fetch_tree_state(message_tree_id)
self._enter_state(mts, message_tree_state.State.ABORTED_LOW_GRADE)
def check_condition_for_prompt_lottery(self, message_tree_id: UUID) -> bool:
logger.debug(f"check_condition_for_prompt_lottery({message_tree_id=})")
mts = self.pr.fetch_tree_state(message_tree_id)
if not mts.active or mts.state != message_tree_state.State.INITIAL_PROMPT_REVIEW:
logger.debug(f"False {mts.active=}, {mts.state=}")
return False
# check if initial prompt was accepted
initial_prompt = self.pr.fetch_message(message_tree_id)
if not initial_prompt.review_result:
logger.debug(f"False {initial_prompt.review_result=}")
return False
self._enter_state(mts, message_tree_state.State.PROMPT_LOTTERY_WAITING)
return True
def check_condition_for_ranking_state(self, message_tree_id: UUID) -> bool:
logger.debug(f"check_condition_for_ranking_state({message_tree_id=})")
mts = self.pr.fetch_tree_state(message_tree_id)
if not mts.active or mts.state != message_tree_state.State.GROWING:
logger.debug(f"False {mts.active=}, {mts.state=}")
return False
# check if desired tree size has been reached and all nodes have been reviewed
tree_size = self.query_tree_size(message_tree_id)
if tree_size.tree_size == 0:
logger.warning(
f"All messages of message tree {message_tree_id} were deleted (tree_size == 0), halting tree."
)
self._enter_state(mts, message_tree_state.State.HALTED_BY_MODERATOR)
return False
if tree_size.remaining_messages > 0 or tree_size.awaiting_review > 0:
logger.debug(f"False {tree_size.remaining_messages=}, {tree_size.awaiting_review=}")
return False
self._enter_state(mts, message_tree_state.State.RANKING)
return True
def check_condition_for_scoring_state(self, message_tree_id: UUID) -> bool:
logger.debug(f"check_condition_for_scoring_state({message_tree_id=})")
mts = self.pr.fetch_tree_state(message_tree_id)
if mts.state != message_tree_state.State.SCORING_FAILED:
if not mts.active or mts.state not in (
message_tree_state.State.RANKING,
message_tree_state.State.READY_FOR_SCORING,
):
logger.debug(f"False {mts.active=}, {mts.state=}")
return False
ranking_role_filter = None if self.cfg.rank_prompter_replies else "assistant"
rankings_by_message = self.query_tree_ranking_results(message_tree_id, role_filter=ranking_role_filter)
for parent_msg_id, ranking in rankings_by_message.items():
if len(ranking) < self.cfg.num_required_rankings:
logger.debug(f"False {parent_msg_id=} {len(ranking)=}")
return False
if (
mts.state != message_tree_state.State.SCORING_FAILED
and mts.state != message_tree_state.State.READY_FOR_SCORING
):
self._enter_state(mts, message_tree_state.State.READY_FOR_SCORING)
self.update_message_ranks(message_tree_id, rankings_by_message)
return True
def ranked_pairs_update(self, rankings: list[MessageReaction]) -> int:
assert len(rankings) > 0
num_updated = 0
ordered_ids_list: list[list[UUID]] = [
msg_reaction.payload.payload.ranked_message_ids for msg_reaction in rankings
]
common_set: set[UUID] = set.intersection(*map(set, ordered_ids_list))
if len(common_set) < 2:
logger.warning("The intersection of ranking results ID sets has less than two elements. Skipping.")
return
# keep only elements in common set
ordered_ids_list = [list(filter(lambda x: x in common_set, ids)) for ids in ordered_ids_list]
assert all(len(x) == len(common_set) for x in ordered_ids_list)
logger.debug(f"SORTED MESSAGE IDS {ordered_ids_list}")
consensus = ranked_pairs(ordered_ids_list)
assert len(consensus) == len(common_set)
logger.debug(f"CONSENSUS: {consensus}\n\n")
# fetch all siblings and index by id
siblings = self.pr.fetch_message_siblings(consensus[0], review_result=None, deleted=None)
siblings = {m.id: m for m in siblings}
# set rank for each message that was part of the common set
for rank, message_id in enumerate(consensus):
message = siblings.get(message_id)
if message:
if message.rank != rank:
message.rank = rank
self.db.add(message)
num_updated += 1
else:
logger.warning(f"Message {message_id=} not found among siblings.")
# clear rank of sibling messages not in consensus
for message in siblings.values():
if message.id not in consensus and message.rank is not None:
message.rank = None
self.db.add(message)
num_updated += 1
return num_updated
def update_message_ranks(
self, message_tree_id: UUID, rankings_by_message: dict[UUID, list[MessageReaction]]
) -> bool:
mts = self.pr.fetch_tree_state(message_tree_id)
# check state, allow retry if in SCORING_FAILED state
if mts.state not in (message_tree_state.State.READY_FOR_SCORING, message_tree_state.State.SCORING_FAILED):
logger.debug(f"False {mts.active=}, {mts.state=}")
return False
if mts.state == message_tree_state.State.SCORING_FAILED:
mts.active = True
mts.state = message_tree_state.State.READY_FOR_SCORING
try:
for rankings in rankings_by_message.values():
if len(rankings) > 0:
self.ranked_pairs_update(rankings)
except Exception:
logger.exception(f"update_message_ranks({message_tree_id=}) failed")
self._enter_state(mts, message_tree_state.State.SCORING_FAILED)
return False
self._enter_state(mts, message_tree_state.State.READY_FOR_EXPORT)
return True
def activate_backlog_tree(self, lang: str) -> MessageTreeState: