feat(examples): tune codesize for triple

examples/triple/tune_size.py

+# -*- coding: utf-8 -*-
+
+# Copyright (c) 2022 Synodic Month, Juni May
+# yaTuner is licensed under Mulan PSL v2.
+# You can use this software according to the terms and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#          http://license.coscl.org.cn/MulanPSL2
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+
+import os
+import yatuner
+
+cc = 'g++'
+src = './src/triple.cpp'
+out = './build/triple.exe'
+base = '-Os'
+
+if not os.path.isdir('./build'):
+    os.mkdir('./build')
+
+optimizers = set(yatuner.utils.fetch_gcc_optimizers(cc=cc)).difference(
+    yatuner.utils.fetch_gcc_enabled_optimizers(options=base))
+# optimizers.remove('-fipa-pta')
+parameters = yatuner.utils.fetch_gcc_parameters(cc=cc)
+
+
+def comp(optimizers, parameters, additional):
+    options = ''
+
+    if additional is not None:
+        options += f'{additional} '
+    else:
+        options += f'{base} '
+
+    if optimizers is not None:
+        for optimizer in optimizers:
+            options += f'{optimizer} '
+
+    if parameters is not None:
+        for parameter, val in parameters.items():
+            options += f'--param={parameter}={val} '
+
+    res = yatuner.utils.execute(f'{cc} {options} {src} -o {out}')
+    if res['returncode'] != 0:
+        raise RuntimeError(res['stderr'])
+
+
+def run():
+    return yatuner.utils.fetch_size(out)
+
+
+def perf():
+    raise NotImplementedError()
+
+
+tuner = yatuner.Tuner(call_compile = comp,
+                      call_running =run,
+                      optimizers = optimizers,
+                      parameters = parameters,
+                      call_perf=perf,
+                      norm_range=0.99,
+                      workspace='yatuner_size.db',
+                      deterministic=True)
+
+tuner.initialize()
+tuner.test_run(num_samples=1, warmup=0)
+tuner.hypotest_optimizers(num_samples=1, num_epochs=30)
+tuner.hypotest_parameters(num_samples=1)
+tuner.optimize(num_samples=1, num_epochs=50)
+tuner.run(num_samples=1)
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