Adding PyGILState_Check() in object_api<>::operator(). (#2919)

* Adding PyGILState_Check() in object_api<>::operator().

* Enabling PyGILState_Check() for Python >= 3.6 only.

Possibly, this explains why PyGILState_Check() cannot safely be used with Python 3.4 and 3.5:

https://github.com/python/cpython/pull/10267#issuecomment-434881587

* Adding simple micro benchmark.

* Reducing test time to minimum (purely for coverage, not for accurate results).

* Fixing silly oversight.

* Minor code organization improvement in test.

* Adding example runtimes.

* Removing capsys (just run with `-k test_callback_num_times -s` and using `.format()`.

include/pybind11/cast.h

@@ -1348,6 +1348,11 @@ unpacking_collector<policy> collect_arguments(Args &&...args) {
 template <typename Derived>
 template <return_value_policy policy, typename... Args>
 object object_api<Derived>::operator()(Args &&...args) const {
+#if !defined(NDEBUG) && PY_VERSION_HEX >= 0x03060000
+    if (!PyGILState_Check()) {
+        pybind11_fail("pybind11::object_api<>::operator() PyGILState_Check() failure.");
+    }
+#endif
     return detail::collect_arguments<policy>(std::forward<Args>(args)...).call(derived().ptr());
 }
 

tests/test_callbacks.cpp

@@ -172,4 +172,10 @@ TEST_SUBMODULE(callbacks, m) {
         for (auto i : work)
             start_f(py::cast<int>(i));
     });
+
+    m.def("callback_num_times", [](py::function f, std::size_t num) {
+        for (std::size_t i = 0; i < num; i++) {
+            f();
+        }
+    });
 }

tests/test_callbacks.py

@@ -2,6 +2,7 @@
 import pytest
 from pybind11_tests import callbacks as m
 from threading import Thread
+import time
 
 
 def test_callbacks():
@@ -146,3 +147,34 @@ def test_async_async_callbacks():
     t = Thread(target=test_async_callbacks)
     t.start()
     t.join()
+
+
+def test_callback_num_times():
+    # Super-simple micro-benchmarking related to PR #2919.
+    # Example runtimes (Intel Xeon 2.2GHz, fully optimized):
+    #   num_millions  1, repeats  2:  0.1 secs
+    #   num_millions 20, repeats 10: 11.5 secs
+    one_million = 1000000
+    num_millions = 1  # Try 20 for actual micro-benchmarking.
+    repeats = 2  # Try 10.
+    rates = []
+    for rep in range(repeats):
+        t0 = time.time()
+        m.callback_num_times(lambda: None, num_millions * one_million)
+        td = time.time() - t0
+        rate = num_millions / td if td else 0
+        rates.append(rate)
+        if not rep:
+            print()
+        print(
+            "callback_num_times: {:d} million / {:.3f} seconds = {:.3f} million / second".format(
+                num_millions, td, rate
+            )
+        )
+    if len(rates) > 1:
+        print("Min    Mean   Max")
+        print(
+            "{:6.3f} {:6.3f} {:6.3f}".format(
+                min(rates), sum(rates) / len(rates), max(rates)
+            )
+        )