用 pybind11 调用 C++

当 Python 需要 C++ 时

交易领域的许多严肃代码都是 C++：撮合引擎、行情处理程序、打磨了十五年的定价库。你很少想重写它们，也很少想为了研究而放弃 Python。pybind11 就是桥梁：这是一个只有头文件的 C++ 库，你写几行话来说“把这个 C++ 函数以这个名字暴露给 Python”，把它们编译成共享库，然后在 Python 里像任何模块一样 import。参数和结果会替你转换：Python 的浮点数列表会变成 std::vector<double>，反之亦然。

# A C++ function and the pybind11 file that exposes it to Python (shown here as text)
cpp = """\
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <vector>

namespace py = pybind11;

double mean(const std::vector<double>& x) {
    double total = 0.0;
    for (double v : x) total += v;
    return x.empty() ? 0.0 : total / x.size();
}

PYBIND11_MODULE(fastmath, m) {
    m.def("mean", &mean, "Mean of a list of floats");
}
"""
lines = cpp.splitlines()
print(lines[-2].strip())                             # m.def("mean", &mean, "Mean of a list of floats");
print(len(lines))                                    # 15

跨越桥梁的类型

每个 Python 类型都有对应的 C++ 类型：int 是 int64_t，float 是 double，str 是 std::string，浮点数列表是 std::vector<double>；其他任何东西都可以作为通用的 py::object 持有。包含 <pybind11/stl.h> 会让标准容器的转换自动完成。大的值用常量引用（const std::vector<double>& x）传递，这样 C++ 函数不会拷贝它们；数字这类小值则按值传递。把 Python 列表转换成向量仍然每次调用会拷贝一次，所以对大数组请使用 py::array_t<double>，它直接查看 NumPy 数组的内存，无需拷贝。

# Choosing the C++ type of each argument; big values are passed by const reference
CPP_TYPES = {"int": "int64_t", "float": "double", "str": "std::string", "list[float]": "std::vector<double>"}

def cpp_type(py_type):
    return CPP_TYPES.get(py_type, "py::object")

def cpp_signature(name, params, returns):
    result = "void" if returns is None else cpp_type(returns)
    arguments = []
    for param, py_type in params:
        ctype = cpp_type(py_type)
        arguments.append(f"const {ctype}& {param}" if ctype.startswith("std::") else f"{ctype} {param}")
    return f"{result} {name}({', '.join(arguments)})"

print(cpp_signature("mean", [("x", "list[float]"), ("n", "int")], "float"))      # double mean(const std::vector<double>& x, int64_t n)
print(cpp_signature("label", [("symbol", "str")], "str"))                       # std::string label(const std::string& symbol)

构建、GIL 与所有权

用 CMake（pybind11_add_module）构建，或在 setup.py 中使用 pybind11.setup_helpers，得到可以 import 的 .so 或 .pyd 文件。有两点需要知道。第一，C++ 代码运行期间，除非用 py::gil_scoped_release 释放，否则 Python 的全局解释器锁（GIL）一直被持有；不接触 Python 对象的长时间 C++ 计算应当释放它，让其他 Python 线程得以运行。第二是所有权：如果 C++ 返回指向自身内存的引用或指针，Python 的使用就不能比它活得更久（由 py::return_value_policy 控制），而没有 Python 回溯信息的崩溃通常意味着悬空指针。像前面的实验一样，要从 Python 里把绑定后的函数与纯 Python 版本对比测试。

# Binding lines and the module file, generated from a table of functions
BACKSLASH = chr(92)

def binding_line(py_name, cpp_name, doc):
    escaped = doc.replace(BACKSLASH, BACKSLASH + BACKSLASH).replace('"', BACKSLASH + '"')
    return f'm.def("{py_name}", &{cpp_name}, "{escaped}");'

def module_source(module, functions):
    lines = ["#include <pybind11/pybind11.h>", "#include <pybind11/stl.h>", "", "namespace py = pybind11;", ""]
    lines.append(f"PYBIND11_MODULE({module}, m) {{")
    for py_name, cpp_name, doc in functions:
        lines.append("    " + binding_line(py_name, cpp_name, doc))
    lines.append("}")
    return "\n".join(lines) + "\n"

source = module_source("pricing", [("mean", "mean_impl", "Mean of x"), ("price", "bs_price", 'Black-Scholes "call" price')])
print(source.splitlines()[-3])                       #     m.def("mean", &mean_impl, "Mean of x");
print(source.splitlines()[-2])                       #     m.def("price", &bs_price, "Black-Scholes \"call\" price");

# From Python the built module is used like any other; this stand-in has the same interface
import types

def mean(x):
    """Mean of a list of floats"""
    return sum(x) / len(x) if x else 0.0

fastmath = types.SimpleNamespace(mean=mean)          # stands in for `import fastmath` after the build
print(fastmath.mean([100.0, 102.0, 101.0]))          # 101.0
print(fastmath.mean.__doc__)                         # Mean of a list of floats   the doc string given to m.def
print(fastmath.mean([]))                             # 0.0   what the C++ code returns for an empty vector

崩溃不是 Python 异常C++ 代码中的 bug 可能带着段错误且不留回溯信息，让整个 Python 进程崩掉，而在交易系统里这意味着进程没了。要在边界处校验收到的大小和数值，并抛出 C++ 异常（pybind11 会把它转成 Python 异常），开发期间用警告和 sanitizer 编译，并让 C++ 暴露面保持很小：少数几个经过充分测试、承担繁重工作的函数，由 Python 负责编排。

pybind11 用 PYBIND11_MODULE 和 m.def(...) 行把 C++ 函数暴露给 Python；结果是可导入的 .so/.pyd 模块。
类型对应：int→int64_t，float→double，str→std::string，列表→std::vector；大值用常量引用，NumPy 数组用 py::array_t。
长时间纯 C++ 工作要释放 GIL，留意返回内存归谁所有，在边界处校验输入，并与纯 Python 版本对照测试。

下一节：线程与 GIL，Python 线程能并行做什么、不能做什么。

实验：评分内容

用 cpp_type 把 Python 类型映射到 C++25 分
用 cpp_signature 声明函数25 分
用 binding_line 暴露函数25 分
用 module_source 组装模块25 分

# A C++ function and the pybind11 file that exposes it to Python (shown here as text) cpp = """\ #include <pybind11/pybind11.h> #include <pybind11/stl.h> #include <vector> namespace py = pybind11; double mean(const std::vector<double>& x) { double total = 0.0; for (double v : x) total += v; return x.empty() ? 0.0 : total / x.size(); } PYBIND11_MODULE(fastmath, m) { m.def("mean", &mean, "Mean of a list of floats"); } """ lines = cpp.splitlines() print(lines[-2].strip()) # m.def("mean", &mean, "Mean of a list of floats"); print(len(lines)) # 15

# Choosing the C++ type of each argument; big values are passed by const reference CPP_TYPES = {"int": "int64_t", "float": "double", "str": "std::string", "list[float]": "std::vector<double>"} def cpp_type(py_type): return CPP_TYPES.get(py_type, "py::object") def cpp_signature(name, params, returns): result = "void" if returns is None else cpp_type(returns) arguments = [] for param, py_type in params: ctype = cpp_type(py_type) arguments.append(f"const {ctype}& {param}" if ctype.startswith("std::") else f"{ctype} {param}") return f"{result} {name}({', '.join(arguments)})" print(cpp_signature("mean", [("x", "list[float]"), ("n", "int")], "float")) # double mean(const std::vector<double>& x, int64_t n) print(cpp_signature("label", [("symbol", "str")], "str")) # std::string label(const std::string& symbol)

# Binding lines and the module file, generated from a table of functions BACKSLASH = chr(92) def binding_line(py_name, cpp_name, doc): escaped = doc.replace(BACKSLASH, BACKSLASH + BACKSLASH).replace('"', BACKSLASH + '"') return f'm.def("{py_name}", &{cpp_name}, "{escaped}");' def module_source(module, functions): lines = ["#include <pybind11/pybind11.h>", "#include <pybind11/stl.h>", "", "namespace py = pybind11;", ""] lines.append(f"PYBIND11_MODULE({module}, m) {{") for py_name, cpp_name, doc in functions: lines.append(" " + binding_line(py_name, cpp_name, doc)) lines.append("}") return "\n".join(lines) + "\n" source = module_source("pricing", [("mean", "mean_impl", "Mean of x"), ("price", "bs_price", 'Black-Scholes "call" price')]) print(source.splitlines()[-3]) # m.def("mean", &mean_impl, "Mean of x"); print(source.splitlines()[-2]) # m.def("price", &bs_price, "Black-Scholes \"call\" price");

# From Python the built module is used like any other; this stand-in has the same interface import types def mean(x): """Mean of a list of floats""" return sum(x) / len(x) if x else 0.0 fastmath = types.SimpleNamespace(mean=mean) # stands in for `import fastmath` after the build print(fastmath.mean([100.0, 102.0, 101.0])) # 101.0 print(fastmath.mean.__doc__) # Mean of a list of floats the doc string given to m.def print(fastmath.mean([])) # 0.0 what the C++ code returns for an empty vector

当 Python 需要 C++ 时

跨越桥梁的类型

构建、GIL 与所有权

实验：评分内容

登录后即可运行你的代码。

用 pybind11 调用 C++

当 Python 需要 C++ 时

跨越桥梁的类型

构建、GIL 与所有权

实验：评分内容

登录后即可运行你的代码。