Merge branch 'test_unique_ptr_member' into pr2672_use_smart_holder_as_default

ubench/holder_comparison.cpp

@@ -46,6 +46,7 @@ PYBIND11_SMART_HOLDER_TYPE_CASTERS(hc::nb_sh)
 
 PYBIND11_MODULE(pybind11_ubench_holder_comparison, m) {
     using namespace hc;
+    m.def("sizeof_smart_holder", []() { return sizeof(py::smart_holder); });
     wrap_number_bucket<nb_up, std::unique_ptr<nb_up>>(m, "number_bucket_up");
     wrap_number_bucket<nb_sp, std::shared_ptr<nb_sp>>(m, "number_bucket_sp");
     wrap_number_bucket<nb_pu, padded_unique_ptr<nb_pu>>(m, "number_bucket_pu");

ubench/holder_comparison.py

@@ -54,6 +54,25 @@ def run(args):
         num_samples,
         selected_holder_type,
     )
+    pflush("sizeof_smart_holder:", m.sizeof_smart_holder())
+
+    def find_call_repetitions(
+        callable,
+        time_delta_floor=1.0e-6,
+        target_elapsed_secs_multiplier=1.05,  # Empirical.
+        target_elapsed_secs_tolerance=0.05,
+        max_iterations=100,
+    ):
+        td_target = (
+            call_repetitions_target_elapsed_secs * target_elapsed_secs_multiplier
+        )
+        crd = call_repetitions_first_pass
+        for _ in range(max_iterations):
+            td = callable(crd)
+            crd = max(1, int(td_target * crd / max(td, time_delta_floor)))
+            if abs(td - td_target) / td_target < target_elapsed_secs_tolerance:
+                return crd
+        raise RuntimeError("find_call_repetitions failure: max_iterations exceeded.")
 
     for size_exponent in range(
         size_exponent_min, size_exponent_max + 1, size_exponent_step
@@ -61,7 +80,7 @@ def run(args):
         data_size = 2 ** size_exponent
         pflush(data_size, "data_size")
         ratios = collections.defaultdict(list)
-        call_repetitions_dynamic = None
+        call_repetitions = None
         for _ in range(num_samples):
             row_0 = None
             for nb_label, nb_type in [
@@ -77,32 +96,27 @@ def run(args):
                     continue
                 nb1 = nb_type(data_size)
                 nb2 = nb_type(data_size)
-                if call_repetitions_dynamic is None:
+
+                def many_sum(call_repetitions):
                     assert int(round(nb1.sum())) == data_size
                     t0 = time.time()
-                    for _ in range(call_repetitions_first_pass):
+                    for _ in range(call_repetitions):
                         nb1.sum()
-                    td_sum = time.time() - t0
-                    call_repetitions_dynamic = max(
-                        call_repetitions_first_pass,
-                        int(
-                            call_repetitions_target_elapsed_secs
-                            * call_repetitions_first_pass
-                            / max(td_sum, 1.0e-6)
-                        )
-                        + 1,
-                    )
-                    pflush(call_repetitions_dynamic, "call_repetitions_dynamic")
-                assert int(round(nb1.sum())) == data_size
-                t0 = time.time()
-                for _ in range(call_repetitions_dynamic):
-                    nb1.sum()
-                td_sum = time.time() - t0
-                assert nb1.add(nb2) == data_size
-                t0 = time.time()
-                for _ in range(call_repetitions_dynamic):
-                    nb1.add(nb2)
-                td_add = time.time() - t0
+                    return time.time() - t0
+
+                def many_add(call_repetitions):
+                    assert nb1.add(nb2) == data_size
+                    t0 = time.time()
+                    for _ in range(call_repetitions):
+                        nb1.add(nb2)
+                    return time.time() - t0
+
+                if call_repetitions is None:
+                    call_repetitions = find_call_repetitions(many_sum)
+                    pflush(call_repetitions, "call_repetitions")
+
+                td_sum = many_sum(call_repetitions)
+                td_add = many_add(call_repetitions)
                 row = [td_sum, td_add]
                 if row_0 is None:
                     pflush("     Sum   Add  ratS  ratA")

ubench/holder_comparison_extract_sheet_data.py

+# -*- coding: utf-8 -*-
+"""Extract mean ratios from holder_comparison.py output."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+
+def run(args):
+    assert len(args) == 1, "log_holder_comparison.txt"
+
+    log_lines = open(args[0]).read().splitlines()
+
+    for ratx in ("_ratS ", "_ratA "):
+        print(ratx)
+        header = None
+        header_row = None
+        data_row = None
+        data_row_buffer = []
+
+        def show():
+            if header_row:
+                if header is None:
+                    print(",".join(header_row))
+                else:
+                    assert header == header_row
+            if data_row is not None:
+                print(",".join(data_row))
+                data_row_buffer.append(data_row)
+            return header_row
+
+        for line in log_lines:
+            if line.endswith(" data_size"):
+                header = show()
+                flds = line.split()
+                assert len(flds) == 2
+                header_row = ["data_size"]
+                data_row = [flds[0]]
+            elif line.endswith(" call_repetitions"):
+                flds = line.split()
+                assert len(flds) == 2
+                header_row.append("calls")
+                data_row.append(flds[0])
+                header_row.append("up")
+                data_row.append("1.000")
+            elif line[2:].startswith(ratx):
+                flds = line.split()
+                assert len(flds) == 4
+                header_row.append(line[:2])
+                data_row.append(flds[2])
+        show()
+
+        print("Scaled to last column:")
+        print(",".join(header_row))
+        for data_row in data_row_buffer:
+            data_row_rescaled = data_row[:2]
+            unit = float(data_row[-1])
+            for fld in data_row[2:]:
+                data_row_rescaled.append("%.3f" % (float(fld) / unit))
+            print(",".join(data_row_rescaled))
+
+
+if __name__ == "__main__":
+    run(args=sys.argv[1:])

ubench/python/number_bucket.clif

+from "pybind11/ubench/number_bucket.h":
+  namespace `pybind11_ubench`:
+    class `number_bucket<0>` as number_bucket_pc:
+      def __init__(self, data_size: int = default)
+      def sum(self) -> float
+      def add(self, other: number_bucket_pc) -> int