Parallel threads schedule tasks predictably.

once_test.py

@@ -1,15 +1,18 @@
 """Unit tests for once decorators."""
 # pylint: disable=missing-function-docstring
 import asyncio
+import collections.abc
 import concurrent.futures
 import functools
 import gc
 import inspect
+import itertools
 import math
 import sys
 import threading
 import time
 import unittest
+import uuid
 import weakref
 
 import once
@@ -31,6 +34,74 @@ async def anext(iter, default=StopAsyncIteration):
 _N_WORKERS = 16
 
 
+class WrappedException:
+    def __init__(self, exception):
+        self.exception = exception
+
+
+def parallel_map(
+    test: unittest.TestCase,
+    func: collections.abc.Callable,
+    # would be collections.abc.Iterable[tuple] | None on py >= 3.10
+    call_args=None,
+    n_threads: int = _N_WORKERS,
+    timeout: float = 10.0,
+) -> list:
+    """Run a function multiple times in parallel.
+
+    We ensure that N parallel tasks are all launched at the "same time", which
+    means all have parallel threads which are released to the GIL to execute at
+    the same time.
+    Why?
+    We can't rely on the thread pool excector to always spin up the full list of _N_WORKERS.
+    In pypy, we have observed that even with blocked tasks, the same thread executes multiple
+    function calls. This lets us handle the scheduling in a predictable way for testing.
+    """
+    if call_args is None:
+        call_args = (tuple() for _ in range(n_threads))
+
+    batches = [[] for i in range(n_threads)]  # type: list[list[tuple[int, tuple]]]
+    for i, call_args in enumerate(call_args):
+        if not isinstance(call_args, tuple):
+            raise TypeError("call arguments must be a tuple")
+        batches[i % n_threads].append((i, call_args))
+    n_calls = i + 1
+    unset = object()
+    results_lock = threading.Lock()
+    results = [unset for _ in range(n_calls)]
+
+    # This barrier is used to ensure that all calls release together, after this function has
+    # completed its setup of creating them.
+    start_barrier = threading.Barrier(min(len(batches), n_calls))
+
+    def wrapped_fn(batch):
+        start_barrier.wait()
+        for index, args in batch:
+            try:
+                result = func(*args)
+            except Exception as e:
+                result = WrappedException(e)
+            with results_lock:
+                results[index] = result
+
+    # We manually set thread names for easier debugging.
+    invocation_id = str(uuid.uuid4())
+    threads = [
+        threading.Thread(target=wrapped_fn, args=[batch], name=f"{test.id()}-{i}-{invocation_id}")
+        for i, batch in enumerate(batches)
+    ]
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join(timeout=timeout)
+    for i, result in enumerate(results):
+        if result is unset:
+            test.fail(f"Call {i} did not complete succesfully")
+        elif isinstance(result, WrappedException):
+            raise result.exception
+    return results
+
+
 class Counter:
     """Holding object for a counter.
 
@@ -388,8 +459,11 @@ def yielding_iterator():
             for _ in range(3):
                 yield counter.get_incremented()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = executor.map(lambda _: list(yielding_iterator()), range(_N_WORKERS * 2))
+        results = parallel_map(
+            self,
+            lambda: list(yielding_iterator()),
+            (tuple() for _ in range(_N_WORKERS * 2)),
+        )
         for result in results:
             self.assertEqual(result, [1, 2, 3])
 
@@ -422,9 +496,7 @@ def yielding_iterator():
     def test_threaded_single_function(self):
         counting_fn, counter = generate_once_counter_fn()
         barrier_counting_fn = execute_with_barrier(counting_fn, n_workers=_N_WORKERS)
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results_generator = executor.map(barrier_counting_fn, range(_N_WORKERS))
-            results = list(results_generator)
+        results = parallel_map(self, barrier_counting_fn)
         self.assertEqual(len(results), _N_WORKERS)
         for r in results:
             self.assertEqual(r, 1)
@@ -433,7 +505,6 @@ def test_threaded_single_function(self):
     def test_once_per_thread(self):
         counter = Counter()
 
-        @execute_with_barrier(n_workers=_N_WORKERS)  # increases chance of a race
         @once.once(per_thread=True)
         @execute_with_barrier(n_workers=_N_WORKERS)
         def counting_fn(*args) -> int:
@@ -442,8 +513,7 @@ def counting_fn(*args) -> int:
             del args
             return counter.get_incremented()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(counting_fn, range(_N_WORKERS * 4)))
+        results = parallel_map(self, counting_fn, (tuple() for _ in range(_N_WORKERS * 4)))
         self.assertEqual(min(results), 1)
         self.assertEqual(max(results), _N_WORKERS)
 
@@ -565,12 +635,10 @@ def once_fn(self):
 
         once_obj = _CallOnceClass()
 
-        @execute_with_barrier(n_workers=_N_WORKERS)  # increases chance of a race
-        def execute(_):
+        def execute():
             return once_obj.once_fn()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(execute, range(_N_WORKERS * 4)))
+        results = parallel_map(self, execute, (tuple() for _ in range(_N_WORKERS * 4)))
         self.assertEqual(min(results), 1)
         self.assertEqual(max(results), 1)
 
@@ -583,12 +651,10 @@ def once_fn(self):
 
         once_obj = _CallOnceClass()
 
-        @execute_with_barrier(n_workers=_N_WORKERS)  # increases chance of a race
-        def execute(_):
+        def execute():
             return once_obj.once_fn()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(execute, range(_N_WORKERS * 4)))
+        results = parallel_map(self, execute, (tuple() for _ in range(_N_WORKERS * 4)))
         self.assertEqual(min(results), 1)
         self.assertEqual(max(results), _N_WORKERS)
 
@@ -697,8 +763,7 @@ def once_fn(self):
         def execute(i):
             return once_objs[i % 4].once_fn()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(execute, range(_N_WORKERS * 4)))
+        results = parallel_map(self, execute, ((i,) for i in range(_N_WORKERS * 4)))
         self.assertEqual(min(results), 1)
         self.assertEqual(max(results), 1)
 
@@ -711,12 +776,10 @@ def once_fn(self):
 
         once_objs = [_CallOnceClass(), _CallOnceClass(), _CallOnceClass(), _CallOnceClass()]
 
-        @execute_with_barrier(n_workers=_N_WORKERS)  # increases chance of a race
         def execute(i):
             return once_objs[i % 4].once_fn()
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(execute, range(_N_WORKERS)))
+        results = parallel_map(self, execute, ((i,) for i in range(_N_WORKERS)))
         self.assertEqual(min(results), 1)
         self.assertEqual(max(results), math.ceil(_N_WORKERS / 4))
 
@@ -776,7 +839,7 @@ def receiving_iterator():
 
         barrier = threading.Barrier(_N_WORKERS)
 
-        def call_iterator(_):
+        def call_iterator():
             gen = receiving_iterator()
             result = []
             barrier.wait()
@@ -785,8 +848,7 @@ def call_iterator(_):
                 result.append(gen.send(i))
             return result
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = executor.map(call_iterator, range(_N_WORKERS))
+        results = parallel_map(self, call_iterator)
         for result in results:
             self.assertEqual(result, list(range(_N_WORKERS * 4)))
 
@@ -811,8 +873,7 @@ def call_iterator(n):
 
         # Unlike the previous test, each execution should yield lists of different lengths.
         # This ensures that the iterator does not hang, even if not exhausted
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = executor.map(call_iterator, range(1, _N_WORKERS + 1))
+        results = parallel_map(self, call_iterator, ((i,) for i in range(1, _N_WORKERS + 1)))
         for i, result in enumerate(results):
             self.assertEqual(result, list(range(i + 1)))
 
@@ -850,16 +911,24 @@ async def counting_fn(*args) -> int:
             del args
             return counter.get_incremented()
 
-        @execute_with_barrier(n_workers=_N_WORKERS)  # increases chance of a race
+        results_lock = asyncio.Lock()
+        results = []
+
+        @execute_with_barrier(n_workers=_N_WORKERS, is_async=True)  # increases chance of a race
+        async def counting_fn_multiple_caller(*args):
+            """Calls counting_fn() multiple times ensuring identical result."""
+            result = await counting_fn()
+            for i in range(5):
+                self.assertEqual(await counting_fn(), result)
+            async with results_lock:
+                results.append(result)
+            return result
+
         def execute(*args):
-            coro = counting_fn(*args)
+            coro = counting_fn_multiple_caller(*args)
             return asyncio.run(coro)
 
-        with concurrent.futures.ThreadPoolExecutor(max_workers=_N_WORKERS) as executor:
-            results = list(executor.map(execute, range(_N_WORKERS)))
-            self.assertEqual(sorted(results), list(range(1, _N_WORKERS + 1)))
-            results = list(executor.map(execute, range(_N_WORKERS)))
-            self.assertEqual(sorted(results), list(range(1, _N_WORKERS + 1)))
+        parallel_map(self, execute)
 
     async def test_failing_function(self):
         counter = Counter()