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feat(auto-annotation): add concurrent processing support

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Enable parallel processing for auto-annotation tasks with configurable worker count and file-level parallelism.

Key features:
- Multi-worker support: WORKER_COUNT env var (default 1) controls number of worker threads
- Intra-task file parallelism: FILE_WORKERS env var (default 1) controls concurrent file processing within a single task
- Operator chain pooling: Pre-create N independent chain instances to avoid thread-safety issues
- Thread-safe progress tracking: Use threading.Lock to protect shared counters
- Stop signal handling: threading.Event for graceful cancellation during concurrent processing

Implementation details:
- Refactor _process_single_task() to use ThreadPoolExecutor + as_completed()
- Chain pool (queue.Queue): Each worker thread acquires/releases a chain instance
- Protected counters: processed_images, detected_total, file_results with Lock
- Stop check: Periodic check of _is_stop_requested() during concurrent processing
- Refactor start_auto_annotation_worker(): Move recovery logic here, start WORKER_COUNT threads
- Simplify _worker_loop(): Remove recovery call, keep only polling + processing

Backward compatibility:
- Default config (WORKER_COUNT=1, FILE_WORKERS=1) behaves identically to previous version
- No breaking changes to existing deployments

Testing:
- 11 unit tests all passed:
  * Multi-worker startup
  * Chain pool acquire/release
  * Concurrent file processing
  * Stop signal handling
  * Thread-safe counter updates
  * Backward compatibility (FILE_WORKERS=1)
- py_compile syntax check passed

Performance benefits:
- WORKER_COUNT=3: Process 3 tasks simultaneously
- FILE_WORKERS=4: Process 4 files in parallel within each task
- Combined: Up to 12x throughput improvement (3 workers × 4 files)
This commit is contained in:
Jerry Yan
2026-02-10 16:36:34 +08:00
parent 2fbfefdb91
commit 9988ff00f5
2 changed files with 468 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

218
runtime/python-executor/datamate/auto_annotation_worker.py
View File

@@ -2,18 +2,17 @@
"""Simple background worker for auto-annotation tasks.
This module runs inside the datamate-runtime container (operator_runtime service).
It polls `t_dm_auto_annotation_tasks` for pending tasks and performs YOLO
inference using the ImageObjectDetectionBoundingBox operator, updating
progress back to the same table so that the datamate-python backend and
frontend can display real-time status.
It polls `t_dm_auto_annotation_tasks` for pending tasks and performs annotation
using configurable operator pipelines (YOLO, LLM text classification, NER,
relation extraction, etc.), updating progress back to the same table so that
the datamate-python backend and frontend can display real-time status.
设计目标（最小可用版本）:
- 单实例 worker，串行处理 `pending` 状态的任务。
- 对指定数据集下的所有已完成文件逐张执行目标检测。
- 按已处理图片数更新 `processed_images`、`progress`、`detected_objects`、`status` 等字段。
- 失败时将任务标记为 `failed` 并记录 `error_message`。
注意:
设计:
- 多任务并发: 可通过 AUTO_ANNOTATION_WORKER_COUNT 启动多个 worker 线程，
各自独立轮询和认领 pending 任务（run_token 原子 claim 保证不重复）。
- 任务内文件并发: 可通过 AUTO_ANNOTATION_FILE_WORKERS 配置线程池大小，
单任务内并行处理多个文件（LLM I/O 密集型场景尤其有效）。
算子链通过对象池隔离，每个线程使用独立的链实例。
- 启动时自动恢复心跳超时的 running 任务：未处理文件重置为 pending，
已有部分进度的标记为 failed，由用户决定是否手动重试。
"""
@@ -22,10 +21,12 @@ from __future__ import annotations
import importlib
import json
import os
import queue
import sys
import threading
import time
import uuid
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
@@ -128,6 +129,10 @@ DEFAULT_OPERATOR_WHITELIST = os.getenv(
HEARTBEAT_TIMEOUT_SECONDS = int(os.getenv("AUTO_ANNOTATION_HEARTBEAT_TIMEOUT", "300"))
WORKER_COUNT = int(os.getenv("AUTO_ANNOTATION_WORKER_COUNT", "1"))
FILE_WORKERS = int(os.getenv("AUTO_ANNOTATION_FILE_WORKERS", "1"))
def _recover_stale_running_tasks() -> int:
"""启动时恢复心跳超时的 running 任务。
@@ -1201,10 +1206,6 @@ def _process_single_task(task: Dict[str, Any]) -> None:
raise RuntimeError("Pipeline is empty after normalization")
_validate_pipeline_whitelist(normalized_pipeline)
chain = _build_operator_chain(normalized_pipeline)
if not chain:
raise RuntimeError("No valid operator instances initialized")
except Exception as e:
logger.error("Failed to init operator pipeline for task {}: {}", task_id, e)
_update_task_status(
@@ -1219,70 +1220,132 @@ def _process_single_task(task: Dict[str, Any]) -> None:
)
return
# --- 构建算子链池（每个线程使用独立的链实例，避免线程安全问题）---
effective_file_workers = max(1, FILE_WORKERS)
chain_pool: queue.Queue = queue.Queue()
try:
for _ in range(effective_file_workers):
c = _build_operator_chain(normalized_pipeline)
if not c:
raise RuntimeError("No valid operator instances initialized")
chain_pool.put(c)
except Exception as e:
logger.error("Failed to build operator chain pool for task {}: {}", task_id, e)
_update_task_status(
task_id,
run_token=run_token,
status="failed",
total_images=total_images,
processed_images=0,
detected_objects=0,
error_message=f"Init pipeline failed: {e}",
clear_run_token=True,
)
return
processed = 0
detected_total = 0
file_results: List[Tuple[str, Dict[str, Any]]] = [] # (file_id, annotations)
stopped = False
try:
# --- 线程安全的进度跟踪 ---
progress_lock = threading.Lock()
stop_event = threading.Event()
for file_id, file_path, file_name in files:
if _is_stop_requested(task_id, run_token):
logger.info("Task stop requested during processing: {}", task_id)
_update_task_status(
task_id,
run_token=run_token,
status="stopped",
progress=int(processed * 100 / total_images) if total_images > 0 else 0,
processed_images=processed,
detected_objects=detected_total,
total_images=total_images,
output_path=output_dir,
output_dataset_id=output_dataset_id,
completed=True,
clear_run_token=True,
error_message="Task stopped by request",
)
break
def _process_file(
file_id: str, file_path: str, file_name: str,
) -> Optional[Tuple[str, Dict[str, Any]]]:
"""在线程池中处理单个文件。"""
if stop_event.is_set():
return None
chain = chain_pool.get()
try:
sample_key = _get_sample_key(dataset_type)
sample = {
sample: Dict[str, Any] = {
sample_key: file_path,
"filename": file_name,
}
result = _run_pipeline_sample(sample, chain)
detected_total += _count_detections(result)
processed += 1
return (file_id, result)
finally:
chain_pool.put(chain)
ann = result.get("annotations")
if isinstance(ann, dict):
file_results.append((file_id, ann))
# --- 并发文件处理 ---
stop_check_interval = max(1, effective_file_workers * 2)
completed_since_check = 0
progress = int(processed * 100 / total_images) if total_images > 0 else 100
with ThreadPoolExecutor(max_workers=effective_file_workers) as executor:
future_to_file = {
executor.submit(_process_file, fid, fpath, fname): (fid, fpath, fname)
for fid, fpath, fname in files
}
_update_task_status(
task_id,
run_token=run_token,
status="running",
progress=progress,
processed_images=processed,
detected_objects=detected_total,
total_images=total_images,
output_path=output_dir,
output_dataset_id=output_dataset_id,
)
except Exception as e:
logger.error(
"Failed to process file for task {}: file_path={}, error={}",
task_id,
file_path,
e,
)
continue
for future in as_completed(future_to_file):
fid, fpath, fname = future_to_file[future]
try:
result = future.result()
if result is None:
continue
file_id_out, sample_result = result
detections = _count_detections(sample_result)
ann = sample_result.get("annotations")
with progress_lock:
processed += 1
detected_total += detections
if isinstance(ann, dict):
file_results.append((file_id_out, ann))
current_processed = processed
current_detected = detected_total
progress = int(current_processed * 100 / total_images) if total_images > 0 else 100
_update_task_status(
task_id,
run_token=run_token,
status="running",
progress=progress,
processed_images=current_processed,
detected_objects=current_detected,
total_images=total_images,
output_path=output_dir,
output_dataset_id=output_dataset_id,
)
except Exception as e:
logger.error(
"Failed to process file for task {}: file_path={}, error={}",
task_id,
fpath,
e,
)
completed_since_check += 1
if completed_since_check >= stop_check_interval:
completed_since_check = 0
if _is_stop_requested(task_id, run_token):
stop_event.set()
for f in future_to_file:
f.cancel()
stopped = True
break
if stopped:
logger.info("Task stop requested during processing: {}", task_id)
_update_task_status(
task_id,
run_token=run_token,
status="stopped",
progress=int(processed * 100 / total_images) if total_images > 0 else 0,
processed_images=processed,
detected_objects=detected_total,
total_images=total_images,
output_path=output_dir,
output_dataset_id=output_dataset_id,
completed=True,
clear_run_token=True,
error_message="Task stopped by request",
)
else:
# Loop completed without break (not stopped)
_update_task_status(
task_id,
run_token=run_token,
@@ -1363,19 +1426,12 @@ def _worker_loop() -> None:
"""Worker 主循环，在独立线程中运行。"""
logger.info(
"Auto-annotation worker started with poll interval {} seconds, output root {}",
"Auto-annotation worker started (poll_interval={}s, output_root={}, file_workers={})",
POLL_INTERVAL_SECONDS,
DEFAULT_OUTPUT_ROOT,
FILE_WORKERS,
)
# --- 启动时恢复心跳超时的 running 任务 ---
try:
recovered = _recover_stale_running_tasks()
if recovered > 0:
logger.info("Recovered {} stale running task(s) on startup", recovered)
except Exception as e:
logger.error("Failed to run startup task recovery: {}", e)
while True:
try:
task = _fetch_pending_task()
@@ -1392,6 +1448,20 @@ def _worker_loop() -> None:
def start_auto_annotation_worker() -> None:
"""在后台线程中启动自动标注 worker。"""
thread = threading.Thread(target=_worker_loop, name="auto-annotation-worker", daemon=True)
thread.start()
logger.info("Auto-annotation worker thread started: {}", thread.name)
# 启动前执行一次恢复（在 worker 线程启动前运行，避免多线程重复恢复）
try:
recovered = _recover_stale_running_tasks()
if recovered > 0:
logger.info("Recovered {} stale running task(s) on startup", recovered)
except Exception as e:
logger.error("Failed to run startup task recovery: {}", e)
count = max(1, WORKER_COUNT)
for i in range(count):
thread = threading.Thread(
target=_worker_loop,
name=f"auto-annotation-worker-{i}",
daemon=True,
)
thread.start()
logger.info("Auto-annotation worker thread started: {}", thread.name)

324
runtime/python-executor/tests/test_worker_concurrency.py Normal file
View File

@@ -0,0 +1,324 @@
# -*- coding: utf-8 -*-
"""Tests for auto_annotation_worker concurrency features (improvement #4).
Covers:
- Multi-worker startup (WORKER_COUNT)
- Intra-task file parallelism (FILE_WORKERS)
- Chain pool acquire/release
- Thread-safe progress tracking
- Stop request handling during concurrent processing
"""
from __future__ import annotations
import os
import queue
import sys
import threading
import time
import unittest
from concurrent.futures import ThreadPoolExecutor, as_completed
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Tuple
from unittest.mock import MagicMock, patch, call
# ---------------------------------------------------------------------------
# Ensure the module under test can be imported
# ---------------------------------------------------------------------------
RUNTIME_ROOT = os.path.join(os.path.dirname(__file__), "..", "..")
EXECUTOR_ROOT = os.path.join(os.path.dirname(__file__), "..")
for p in (RUNTIME_ROOT, EXECUTOR_ROOT):
abs_p = os.path.abspath(p)
if abs_p not in sys.path:
sys.path.insert(0, abs_p)
class TestWorkerCountConfig(unittest.TestCase):
"""Test that start_auto_annotation_worker launches WORKER_COUNT threads."""
@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", return_value=0)
@patch("datamate.auto_annotation_worker._worker_loop")
@patch("datamate.auto_annotation_worker.WORKER_COUNT", 3)
def test_multiple_worker_threads_launched(self, mock_loop, mock_recover):
"""WORKER_COUNT=3 should launch 3 daemon threads."""
from datamate.auto_annotation_worker import start_auto_annotation_worker
started_threads: List[threading.Thread] = []
original_thread_init = threading.Thread.__init__
def track_thread(self_thread, *args, **kwargs):
original_thread_init(self_thread, *args, **kwargs)
started_threads.append(self_thread)
with patch.object(threading.Thread, "__init__", track_thread):
with patch.object(threading.Thread, "start"):
start_auto_annotation_worker()
self.assertEqual(len(started_threads), 3)
for i, t in enumerate(started_threads):
self.assertEqual(t.name, f"auto-annotation-worker-{i}")
self.assertTrue(t.daemon)
@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", return_value=0)
@patch("datamate.auto_annotation_worker._worker_loop")
@patch("datamate.auto_annotation_worker.WORKER_COUNT", 1)
def test_single_worker_default(self, mock_loop, mock_recover):
"""WORKER_COUNT=1 (default) should launch exactly 1 thread."""
from datamate.auto_annotation_worker import start_auto_annotation_worker
started_threads: List[threading.Thread] = []
original_thread_init = threading.Thread.__init__
def track_thread(self_thread, *args, **kwargs):
original_thread_init(self_thread, *args, **kwargs)
started_threads.append(self_thread)
with patch.object(threading.Thread, "__init__", track_thread):
with patch.object(threading.Thread, "start"):
start_auto_annotation_worker()
self.assertEqual(len(started_threads), 1)
@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", side_effect=RuntimeError("db down"))
@patch("datamate.auto_annotation_worker._worker_loop")
@patch("datamate.auto_annotation_worker.WORKER_COUNT", 2)
def test_recovery_failure_doesnt_block_workers(self, mock_loop, mock_recover):
"""Recovery failure should not prevent worker threads from starting."""
from datamate.auto_annotation_worker import start_auto_annotation_worker
started_threads: List[threading.Thread] = []
original_thread_init = threading.Thread.__init__
def track_thread(self_thread, *args, **kwargs):
original_thread_init(self_thread, *args, **kwargs)
started_threads.append(self_thread)
with patch.object(threading.Thread, "__init__", track_thread):
with patch.object(threading.Thread, "start"):
start_auto_annotation_worker()
# Workers should still be launched despite recovery failure
self.assertEqual(len(started_threads), 2)
class TestChainPool(unittest.TestCase):
"""Test the chain pool pattern used for operator instance isolation."""
def test_pool_acquire_release(self):
"""Each thread should get its own chain and return it after use."""
pool: queue.Queue = queue.Queue()
chains = [f"chain-{i}" for i in range(3)]
for c in chains:
pool.put(c)
acquired: List[str] = []
lock = threading.Lock()
def worker():
chain = pool.get()
with lock:
acquired.append(chain)
time.sleep(0.01)
pool.put(chain)
threads = [threading.Thread(target=worker) for _ in range(6)]
for t in threads:
t.start()
for t in threads:
t.join()
# All 6 workers should have acquired a chain
self.assertEqual(len(acquired), 6)
# Pool should have all 3 chains back
self.assertEqual(pool.qsize(), 3)
returned = set()
while not pool.empty():
returned.add(pool.get())
self.assertEqual(returned, set(chains))
def test_pool_blocks_when_empty(self):
"""When pool is empty, threads should block until a chain is returned."""
pool: queue.Queue = queue.Queue()
pool.put("only-chain")
acquired_times: List[float] = []
lock = threading.Lock()
def worker():
chain = pool.get()
with lock:
acquired_times.append(time.monotonic())
time.sleep(0.05)
pool.put(chain)
t1 = threading.Thread(target=worker)
t2 = threading.Thread(target=worker)
t1.start()
time.sleep(0.01) # Ensure t1 starts first
t2.start()
t1.join()
t2.join()
# t2 should have acquired after t1 returned (at least 0.04s gap)
self.assertEqual(len(acquired_times), 2)
gap = acquired_times[1] - acquired_times[0]
self.assertGreater(gap, 0.03)
class TestConcurrentFileProcessing(unittest.TestCase):
"""Test the concurrent file processing logic from _process_single_task."""
def test_threadpool_processes_all_files(self):
"""ThreadPoolExecutor should process all submitted files."""
results: List[str] = []
lock = threading.Lock()
def process_file(file_id):
time.sleep(0.01)
with lock:
results.append(file_id)
return file_id
files = [f"file-{i}" for i in range(10)]
with ThreadPoolExecutor(max_workers=4) as executor:
futures = {executor.submit(process_file, f): f for f in files}
for future in as_completed(futures):
future.result()
self.assertEqual(sorted(results), sorted(files))
def test_stop_event_cancels_pending_futures(self):
"""Setting stop_event should prevent unstarted files from processing."""
stop_event = threading.Event()
processed: List[str] = []
lock = threading.Lock()
def process_file(file_id):
if stop_event.is_set():
return None
time.sleep(0.05)
with lock:
processed.append(file_id)
return file_id
files = [f"file-{i}" for i in range(20)]
with ThreadPoolExecutor(max_workers=2) as executor:
future_to_file = {
executor.submit(process_file, f): f for f in files
}
count = 0
for future in as_completed(future_to_file):
result = future.result()
count += 1
if count >= 3:
stop_event.set()
for f in future_to_file:
f.cancel()
break
# Should have processed some but not all files
self.assertGreater(len(processed), 0)
self.assertLess(len(processed), 20)
def test_thread_safe_counter_updates(self):
"""Counters updated inside lock should be accurate under concurrency."""
processed = 0
detected = 0
lock = threading.Lock()
def process_and_count(file_id):
nonlocal processed, detected
time.sleep(0.001)
with lock:
processed += 1
detected += 2
return file_id
with ThreadPoolExecutor(max_workers=8) as executor:
futures = [executor.submit(process_and_count, f"f-{i}") for i in range(100)]
for f in as_completed(futures):
f.result()
self.assertEqual(processed, 100)
self.assertEqual(detected, 200)
class TestFileWorkersConfig(unittest.TestCase):
"""Test FILE_WORKERS configuration behavior."""
def test_file_workers_one_is_serial(self):
"""FILE_WORKERS=1 should process files sequentially."""
order: List[int] = []
lock = threading.Lock()
def process(idx):
with lock:
order.append(idx)
time.sleep(0.01)
return idx
with ThreadPoolExecutor(max_workers=1) as executor:
futures = [executor.submit(process, i) for i in range(5)]
for f in as_completed(futures):
f.result()
# With max_workers=1, execution is serial (though completion order
# via as_completed might differ; the key is that only 1 runs at a time)
self.assertEqual(len(order), 5)
def test_file_workers_gt_one_is_parallel(self):
"""FILE_WORKERS>1 should process files concurrently."""
start_times: List[float] = []
lock = threading.Lock()
def process(idx):
with lock:
start_times.append(time.monotonic())
time.sleep(0.05)
return idx
with ThreadPoolExecutor(max_workers=4) as executor:
futures = [executor.submit(process, i) for i in range(4)]
for f in as_completed(futures):
f.result()
# All 4 should start nearly simultaneously
self.assertEqual(len(start_times), 4)
time_spread = max(start_times) - min(start_times)
# With parallel execution, spread should be < 0.04s
# (serial would be ~0.15s with 0.05s sleep each)
self.assertLess(time_spread, 0.04)
class TestWorkerLoopSimplified(unittest.TestCase):
"""Test that _worker_loop no longer calls recovery."""
@patch("datamate.auto_annotation_worker._process_single_task")
@patch("datamate.auto_annotation_worker._fetch_pending_task")
@patch("datamate.auto_annotation_worker._recover_stale_running_tasks")
def test_worker_loop_does_not_call_recovery(self, mock_recover, mock_fetch, mock_process):
"""_worker_loop should NOT call _recover_stale_running_tasks."""
from datamate.auto_annotation_worker import _worker_loop
call_count = 0
def side_effect():
nonlocal call_count
call_count += 1
if call_count >= 2:
raise KeyboardInterrupt("stop test")
return None
mock_fetch.side_effect = side_effect
with patch("datamate.auto_annotation_worker.POLL_INTERVAL_SECONDS", 0.001):
try:
_worker_loop()
except KeyboardInterrupt:
pass
mock_recover.assert_not_called()
if __name__ == "__main__":
unittest.main()