Print memory peak message for UT #42092
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你的PR提交成功，感谢你对开源项目的贡献! |
From00
commented
Apr 28, 2022
| @@ -539,7 +536,7 @@ inline void retry_sleep(unsigned milliseconds) { | |||
| ::paddle::platform::details::ExternalApiType< \ | |||
| __CUDA_STATUS_TYPE__>::kSuccess; \ | |||
| while (UNLIKELY(__cond__ != __success_type__) && retry_count < 5) { \ | |||
| paddle::platform::retry_sleep(FLAGS_gpu_allocator_retry_time); \ | |||
| paddle::platform::retry_sleep(10000); \ | |||
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此处enforce.h中的PADDLE_RETRY_CUDA_SUCCESS宏复用了定义在allocator_facade.cc中的FLAGS_gpu_allocator_retry_time，该宏被包括phi算子库在内的许多地方使用，从而引入了对allocator_facade的依赖，而在设计上allocator_facade应该是依赖于phi算子库的，因而此处既存在链接错误的风险，也给后续phi与fluid的完全解耦带来阻碍。经与 @zhhsplendid @chenwhql 讨论后决定直接将PADDLE_RETRY_CUDA_SUCCESS中的FLAGS_gpu_allocator_retry_time替换成固定的默认值10000。
luotao1
approved these changes
May 5, 2022
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Describe
在单测运行结束时打印显存占用峰值信息：
此信息供CI流水线监控单测的显存占用情况，同时也方便RD在本地进行单元测试时了解单测的显存使用情况，后续将考虑据此在CI上拦截显存占用过多的新增单测。
是否打印信息可通过FLAGS_enable_gpu_memory_usage_log环境变量设置，目前全局默认关闭，但在test_runner.py和paddle_gtest_main.cc中会自动开启。