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Jerry Yan
9988ff00f5
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)
325 lines
12 KiB
Python
325 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""Tests for auto_annotation_worker concurrency features (improvement #4).
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Covers:
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- Multi-worker startup (WORKER_COUNT)
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- Intra-task file parallelism (FILE_WORKERS)
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- Chain pool acquire/release
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- Thread-safe progress tracking
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- Stop request handling during concurrent processing
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"""
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from __future__ import annotations
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import os
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import queue
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import sys
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import threading
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import time
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import unittest
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from contextlib import contextmanager
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from typing import Any, Dict, List, Optional, Tuple
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from unittest.mock import MagicMock, patch, call
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# ---------------------------------------------------------------------------
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# Ensure the module under test can be imported
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# ---------------------------------------------------------------------------
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RUNTIME_ROOT = os.path.join(os.path.dirname(__file__), "..", "..")
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EXECUTOR_ROOT = os.path.join(os.path.dirname(__file__), "..")
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for p in (RUNTIME_ROOT, EXECUTOR_ROOT):
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abs_p = os.path.abspath(p)
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if abs_p not in sys.path:
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sys.path.insert(0, abs_p)
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class TestWorkerCountConfig(unittest.TestCase):
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"""Test that start_auto_annotation_worker launches WORKER_COUNT threads."""
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@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", return_value=0)
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@patch("datamate.auto_annotation_worker._worker_loop")
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@patch("datamate.auto_annotation_worker.WORKER_COUNT", 3)
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def test_multiple_worker_threads_launched(self, mock_loop, mock_recover):
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"""WORKER_COUNT=3 should launch 3 daemon threads."""
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from datamate.auto_annotation_worker import start_auto_annotation_worker
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started_threads: List[threading.Thread] = []
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original_thread_init = threading.Thread.__init__
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def track_thread(self_thread, *args, **kwargs):
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original_thread_init(self_thread, *args, **kwargs)
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started_threads.append(self_thread)
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with patch.object(threading.Thread, "__init__", track_thread):
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with patch.object(threading.Thread, "start"):
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start_auto_annotation_worker()
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self.assertEqual(len(started_threads), 3)
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for i, t in enumerate(started_threads):
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self.assertEqual(t.name, f"auto-annotation-worker-{i}")
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self.assertTrue(t.daemon)
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@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", return_value=0)
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@patch("datamate.auto_annotation_worker._worker_loop")
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@patch("datamate.auto_annotation_worker.WORKER_COUNT", 1)
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def test_single_worker_default(self, mock_loop, mock_recover):
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"""WORKER_COUNT=1 (default) should launch exactly 1 thread."""
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from datamate.auto_annotation_worker import start_auto_annotation_worker
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started_threads: List[threading.Thread] = []
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original_thread_init = threading.Thread.__init__
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def track_thread(self_thread, *args, **kwargs):
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original_thread_init(self_thread, *args, **kwargs)
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started_threads.append(self_thread)
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with patch.object(threading.Thread, "__init__", track_thread):
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with patch.object(threading.Thread, "start"):
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start_auto_annotation_worker()
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self.assertEqual(len(started_threads), 1)
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@patch("datamate.auto_annotation_worker._recover_stale_running_tasks", side_effect=RuntimeError("db down"))
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@patch("datamate.auto_annotation_worker._worker_loop")
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@patch("datamate.auto_annotation_worker.WORKER_COUNT", 2)
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def test_recovery_failure_doesnt_block_workers(self, mock_loop, mock_recover):
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"""Recovery failure should not prevent worker threads from starting."""
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from datamate.auto_annotation_worker import start_auto_annotation_worker
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started_threads: List[threading.Thread] = []
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original_thread_init = threading.Thread.__init__
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def track_thread(self_thread, *args, **kwargs):
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original_thread_init(self_thread, *args, **kwargs)
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started_threads.append(self_thread)
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with patch.object(threading.Thread, "__init__", track_thread):
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with patch.object(threading.Thread, "start"):
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start_auto_annotation_worker()
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# Workers should still be launched despite recovery failure
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self.assertEqual(len(started_threads), 2)
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class TestChainPool(unittest.TestCase):
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"""Test the chain pool pattern used for operator instance isolation."""
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def test_pool_acquire_release(self):
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"""Each thread should get its own chain and return it after use."""
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pool: queue.Queue = queue.Queue()
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chains = [f"chain-{i}" for i in range(3)]
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for c in chains:
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pool.put(c)
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acquired: List[str] = []
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lock = threading.Lock()
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def worker():
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chain = pool.get()
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with lock:
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acquired.append(chain)
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time.sleep(0.01)
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pool.put(chain)
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threads = [threading.Thread(target=worker) for _ in range(6)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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# All 6 workers should have acquired a chain
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self.assertEqual(len(acquired), 6)
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# Pool should have all 3 chains back
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self.assertEqual(pool.qsize(), 3)
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returned = set()
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while not pool.empty():
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returned.add(pool.get())
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self.assertEqual(returned, set(chains))
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def test_pool_blocks_when_empty(self):
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"""When pool is empty, threads should block until a chain is returned."""
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pool: queue.Queue = queue.Queue()
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pool.put("only-chain")
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acquired_times: List[float] = []
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lock = threading.Lock()
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def worker():
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chain = pool.get()
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with lock:
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acquired_times.append(time.monotonic())
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time.sleep(0.05)
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pool.put(chain)
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t1 = threading.Thread(target=worker)
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t2 = threading.Thread(target=worker)
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t1.start()
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time.sleep(0.01) # Ensure t1 starts first
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t2.start()
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t1.join()
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t2.join()
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# t2 should have acquired after t1 returned (at least 0.04s gap)
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self.assertEqual(len(acquired_times), 2)
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gap = acquired_times[1] - acquired_times[0]
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self.assertGreater(gap, 0.03)
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class TestConcurrentFileProcessing(unittest.TestCase):
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"""Test the concurrent file processing logic from _process_single_task."""
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def test_threadpool_processes_all_files(self):
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"""ThreadPoolExecutor should process all submitted files."""
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results: List[str] = []
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lock = threading.Lock()
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def process_file(file_id):
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time.sleep(0.01)
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with lock:
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results.append(file_id)
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return file_id
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files = [f"file-{i}" for i in range(10)]
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with ThreadPoolExecutor(max_workers=4) as executor:
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futures = {executor.submit(process_file, f): f for f in files}
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for future in as_completed(futures):
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future.result()
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self.assertEqual(sorted(results), sorted(files))
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def test_stop_event_cancels_pending_futures(self):
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"""Setting stop_event should prevent unstarted files from processing."""
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stop_event = threading.Event()
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processed: List[str] = []
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lock = threading.Lock()
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def process_file(file_id):
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if stop_event.is_set():
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return None
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time.sleep(0.05)
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with lock:
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processed.append(file_id)
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return file_id
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files = [f"file-{i}" for i in range(20)]
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with ThreadPoolExecutor(max_workers=2) as executor:
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future_to_file = {
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executor.submit(process_file, f): f for f in files
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}
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count = 0
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for future in as_completed(future_to_file):
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result = future.result()
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count += 1
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if count >= 3:
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stop_event.set()
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for f in future_to_file:
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f.cancel()
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break
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# Should have processed some but not all files
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self.assertGreater(len(processed), 0)
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self.assertLess(len(processed), 20)
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def test_thread_safe_counter_updates(self):
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"""Counters updated inside lock should be accurate under concurrency."""
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processed = 0
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detected = 0
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lock = threading.Lock()
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def process_and_count(file_id):
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nonlocal processed, detected
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time.sleep(0.001)
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with lock:
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processed += 1
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detected += 2
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return file_id
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with ThreadPoolExecutor(max_workers=8) as executor:
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futures = [executor.submit(process_and_count, f"f-{i}") for i in range(100)]
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for f in as_completed(futures):
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f.result()
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self.assertEqual(processed, 100)
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self.assertEqual(detected, 200)
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class TestFileWorkersConfig(unittest.TestCase):
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"""Test FILE_WORKERS configuration behavior."""
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def test_file_workers_one_is_serial(self):
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"""FILE_WORKERS=1 should process files sequentially."""
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order: List[int] = []
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lock = threading.Lock()
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def process(idx):
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with lock:
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order.append(idx)
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time.sleep(0.01)
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return idx
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with ThreadPoolExecutor(max_workers=1) as executor:
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futures = [executor.submit(process, i) for i in range(5)]
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for f in as_completed(futures):
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f.result()
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# With max_workers=1, execution is serial (though completion order
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# via as_completed might differ; the key is that only 1 runs at a time)
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self.assertEqual(len(order), 5)
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def test_file_workers_gt_one_is_parallel(self):
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"""FILE_WORKERS>1 should process files concurrently."""
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start_times: List[float] = []
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lock = threading.Lock()
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def process(idx):
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with lock:
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start_times.append(time.monotonic())
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time.sleep(0.05)
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return idx
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with ThreadPoolExecutor(max_workers=4) as executor:
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futures = [executor.submit(process, i) for i in range(4)]
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for f in as_completed(futures):
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f.result()
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# All 4 should start nearly simultaneously
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self.assertEqual(len(start_times), 4)
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time_spread = max(start_times) - min(start_times)
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# With parallel execution, spread should be < 0.04s
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# (serial would be ~0.15s with 0.05s sleep each)
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self.assertLess(time_spread, 0.04)
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class TestWorkerLoopSimplified(unittest.TestCase):
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"""Test that _worker_loop no longer calls recovery."""
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@patch("datamate.auto_annotation_worker._process_single_task")
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@patch("datamate.auto_annotation_worker._fetch_pending_task")
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@patch("datamate.auto_annotation_worker._recover_stale_running_tasks")
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def test_worker_loop_does_not_call_recovery(self, mock_recover, mock_fetch, mock_process):
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"""_worker_loop should NOT call _recover_stale_running_tasks."""
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from datamate.auto_annotation_worker import _worker_loop
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call_count = 0
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def side_effect():
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nonlocal call_count
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call_count += 1
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if call_count >= 2:
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raise KeyboardInterrupt("stop test")
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return None
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mock_fetch.side_effect = side_effect
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with patch("datamate.auto_annotation_worker.POLL_INTERVAL_SECONDS", 0.001):
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try:
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_worker_loop()
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except KeyboardInterrupt:
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pass
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mock_recover.assert_not_called()
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if __name__ == "__main__":
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unittest.main()
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