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test/test_functions.py
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test/test_functions.py
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"""
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函数序列化测试 - 验证 cloudpickle 集成
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"""
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import multiprocessing
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import time
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import math
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import pytest
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from mpsp.mpsp import MultiProcessingSharedPool
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# ==================== 模块级别的普通函数 ====================
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def simple_function(x):
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"""简单的加法函数"""
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return x + 1
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def multiply_function(a, b):
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"""乘法函数"""
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return a * b
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def function_with_default(x, y=10):
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"""带默认参数的函数"""
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return x + y
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def function_with_kwargs(*args, **kwargs):
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"""带可变参数的函数"""
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return sum(args) + sum(kwargs.values())
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def recursive_function(n):
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"""递归函数"""
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if n <= 1:
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return 1
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return n * recursive_function(n - 1)
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def closure_factory(base):
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"""闭包工厂函数"""
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def inner(x):
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return x + base
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return inner
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# ==================== 辅助函数（模块级别定义）====================
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def worker_execute_function(key, result_queue):
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"""子进程：获取函数并执行"""
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pool = MultiProcessingSharedPool()
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func = pool.get(key)
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if func is None:
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result_queue.put(None)
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return
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# 执行函数（根据不同的测试函数传入不同参数）
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try:
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if key == "simple_func":
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result = func(5)
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elif key == "multiply_func":
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result = func(3, 4)
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elif key == "default_func":
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result = func(5)
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elif key == "kwargs_func":
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result = func(1, 2, 3, a=4, b=5)
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elif key == "recursive_func":
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result = func(5)
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elif key == "closure_func":
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result = func(10)
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elif key == "lambda_func":
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result = func(7)
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elif key == "lambda_with_capture":
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result = func()
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elif key == "nested_func":
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result = func(3)
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else:
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result = func()
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result_queue.put(result)
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except Exception as e:
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result_queue.put(f"ERROR: {e}")
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def worker_execute_lambda_with_arg(key, arg, result_queue):
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"""子进程：获取 lambda 并执行，传入参数"""
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pool = MultiProcessingSharedPool()
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func = pool.get(key)
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if func is None:
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result_queue.put(None)
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return
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result_queue.put(func(arg))
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def get_lambda_description(func):
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"""获取 lambda 函数的描述字符串"""
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try:
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return func.__name__
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except AttributeError:
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return str(func)
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# ==================== 测试类 ====================
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class TestNormalFunctions:
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"""测试普通函数的序列化和反序列化"""
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def test_simple_function(self):
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"""测试简单函数的传递"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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# 存储函数
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result = pool.put("simple_func", simple_function)
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assert result is True
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# 当前进程验证
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retrieved = pool.get("simple_func")
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assert retrieved is not None
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assert retrieved(5) == 6
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assert retrieved(10) == 11
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def test_simple_function_cross_process(self):
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"""测试简单函数的跨进程传递"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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# 父进程存储函数
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pool.put("simple_func", simple_function)
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# 子进程获取并执行
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("simple_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 6 # simple_function(5) = 5 + 1
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def test_function_with_multiple_args(self):
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"""测试多参数函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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pool.put("multiply_func", multiply_function)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("multiply_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 12 # multiply_function(3, 4) = 12
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def test_function_with_default_args(self):
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"""测试带默认参数的函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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pool.put("default_func", function_with_default)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("default_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 15 # function_with_default(5) = 5 + 10
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def test_function_with_kwargs(self):
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"""测试带 **kwargs 的函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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pool.put("kwargs_func", function_with_kwargs)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("kwargs_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 15 # sum(1,2,3) + sum(4,5) = 6 + 9 = 15
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def test_recursive_function(self):
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"""测试递归函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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pool.put("recursive_func", recursive_function)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("recursive_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 120 # 5! = 120
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class TestLambdaFunctions:
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"""测试 Lambda 函数的序列化和反序列化"""
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def test_simple_lambda(self):
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"""测试简单 lambda 函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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simple_lambda = lambda x: x * 2
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result = pool.put("lambda_func", simple_lambda)
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assert result is True
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# 当前进程验证
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retrieved = pool.get("lambda_func")
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assert retrieved(5) == 10
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assert retrieved(7) == 14
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def test_simple_lambda_cross_process(self):
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"""测试简单 lambda 的跨进程传递"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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simple_lambda = lambda x: x * 3
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pool.put("lambda_func", simple_lambda)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("lambda_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 21 # lambda(7) = 7 * 3 = 21
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def test_lambda_with_capture(self):
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"""测试捕获外部变量的 lambda"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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captured_value = 100
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capturing_lambda = lambda: captured_value + 1
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pool.put("lambda_with_capture", capturing_lambda)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("lambda_with_capture", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 101 # captured_value + 1 = 101
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def test_lambda_in_list_comprehension(self):
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"""测试在列表推导式中创建的 lambda"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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# 创建多个 lambda
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lambdas = [(lambda x, i=i: x + i) for i in range(5)]
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for i, lam in enumerate(lambdas):
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pool.put(f"lambda_{i}", lam)
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# 验证每个 lambda 都能正确捕获各自的 i
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for i in range(5):
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retrieved = pool.get(f"lambda_{i}")
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assert retrieved(10) == 10 + i
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def test_complex_lambda(self):
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"""测试复杂的 lambda 表达式"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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complex_lambda = lambda x, y: (x**2 + y**2) ** 0.5
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pool.put("complex_lambda", complex_lambda)
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# 子进程验证
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def worker_execute_complex_lambda(key, x, y, result_queue):
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pool = MultiProcessingSharedPool()
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func = pool.get(key)
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result_queue.put(func(x, y))
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_complex_lambda,
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args=("complex_lambda", 3, 4, result_queue),
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert abs(result - 5.0) < 1e-10 # sqrt(3^2 + 4^2) = 5
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class TestNestedFunctions:
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"""测试嵌套函数（在函数内部定义的函数）"""
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def test_nested_function(self):
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"""测试嵌套函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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def outer_function(x):
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def inner_function(y):
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return y * y
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return inner_function(x) + x
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pool.put("nested_func", outer_function)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("nested_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 12 # outer_function(3) = 3*3 + 3 = 12
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def test_closure_function(self):
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"""测试闭包函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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closure_func = closure_factory(100)
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pool.put("closure_func", closure_func)
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result_queue = multiprocessing.Queue()
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p = multiprocessing.Process(
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target=worker_execute_function, args=("closure_func", result_queue)
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)
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 110 # closure_func(10) = 10 + 100
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def test_multiple_closures(self):
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"""测试多个闭包"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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closures = [closure_factory(i) for i in range(5)]
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for i, closure in enumerate(closures):
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pool.put(f"closure_{i}", closure)
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# 验证每个闭包捕获的值不同
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for i in range(5):
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retrieved = pool.get(f"closure_{i}")
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assert retrieved(10) == 10 + i
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class TestClassMethods:
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"""测试类方法的序列化"""
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def test_static_method(self):
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"""测试静态方法"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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class Calculator:
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@staticmethod
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def add(x, y):
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return x + y
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@staticmethod
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def multiply(x, y):
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return x * y
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pool.put("static_add", Calculator.add)
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pool.put("static_multiply", Calculator.multiply)
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# 验证静态方法
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add_func = pool.get("static_add")
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multiply_func = pool.get("static_multiply")
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assert add_func(2, 3) == 5
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assert multiply_func(2, 3) == 6
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def test_class_method(self):
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"""测试类方法"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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class Counter:
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count = 0
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@classmethod
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def increment(cls):
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cls.count += 1
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return cls.count
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# 注意：类方法通常不能被 cloudpickle 正确序列化
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# 因为它依赖于类定义
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result = pool.put("class_method", Counter.increment)
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# 如果成功存储，尝试执行
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if result:
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try:
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method = pool.get("class_method")
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# 类方法在反序列化后可能无法正常工作
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# 这取决于 cloudpickle 的实现
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except Exception:
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pass # 预期可能失败
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class TestBuiltInFunctions:
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"""测试内置函数的序列化"""
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def test_builtin_functions(self):
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"""测试 Python 内置函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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# 大多数内置函数可以用标准 pickle 序列化
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pool.put("builtin_sum", sum)
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pool.put("builtin_max", max)
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pool.put("builtin_min", min)
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pool.put("builtin_len", len)
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# 验证
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assert pool.get("builtin_sum")([1, 2, 3]) == 6
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assert pool.get("builtin_max")([1, 2, 3]) == 3
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assert pool.get("builtin_min")([1, 2, 3]) == 1
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assert pool.get("builtin_len")([1, 2, 3]) == 3
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def test_math_functions(self):
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"""测试 math 模块函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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pool.put("math_sqrt", math.sqrt)
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pool.put("math_sin", math.sin)
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pool.put("math_cos", math.cos)
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# 验证
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assert abs(pool.get("math_sqrt")(16) - 4.0) < 1e-10
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assert abs(pool.get("math_sin")(0) - 0.0) < 1e-10
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assert abs(pool.get("math_cos")(0) - 1.0) < 1e-10
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class TestFunctionReturnValues:
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"""测试函数作为返回值"""
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def test_function_returned_from_function(self):
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"""测试返回函数的函数"""
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pool = MultiProcessingSharedPool()
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pool.clear()
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def create_multiplier(factor):
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return lambda x: x * factor
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pool.put("create_multiplier", create_multiplier)
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# 在子进程中获取并使用
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def worker_get_multiplier(result_queue):
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pool = MultiProcessingSharedPool()
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factory = pool.get("create_multiplier")
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multiplier_func = factory(5) # 创建一个乘以 5 的函数
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result_queue.put(multiplier_func(10))
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result_queue = multiprocessing.Queue()
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||||
p = multiprocessing.Process(target=worker_get_multiplier, args=(result_queue,))
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p.start()
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p.join()
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result = result_queue.get()
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assert result == 50 # 10 * 5 = 50
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||||
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||||
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||||
class TestErrorHandling:
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"""测试函数序列化的错误处理"""
|
||||
|
||||
def test_unpicklable_function_fallback(self):
|
||||
"""测试无法序列化的函数回退到 cloudpickle"""
|
||||
pool = MultiProcessingSharedPool()
|
||||
pool.clear()
|
||||
|
||||
# 创建局部函数（无法被标准 pickle 序列化）
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||||
def local_function(x):
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return x**2
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||||
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||||
# 应该通过 cloudpickle 成功存储
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||||
result = pool.put("local_func", local_function)
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assert result is True
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||||
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||||
# 验证可以正确执行
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retrieved = pool.get("local_func")
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assert retrieved(5) == 25
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||||
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||||
def test_function_with_unpicklable_capture(self):
|
||||
"""测试捕获不可序列化对象的函数"""
|
||||
pool = MultiProcessingSharedPool()
|
||||
pool.clear()
|
||||
|
||||
# 捕获文件对象（不可序列化）
|
||||
try:
|
||||
with open(__file__, "r") as f:
|
||||
file_capturing_lambda = lambda: f.read()
|
||||
|
||||
# 尝试存储应该失败
|
||||
result = pool.put("file_lambda", file_capturing_lambda)
|
||||
# 如果 cloudpickle 支持，验证是否能正确失败
|
||||
except Exception:
|
||||
pass # 预期可能失败
|
||||
Reference in New Issue
Block a user