pytorch/test/cpp/monitor/test_counters.cpp
Tristan Rice 758d7dea9c torch.monitor - Initial C++ Stats (#68074)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68074

This is the first step of many PRs towards implementing the `torch.monitor` RFC https://github.com/pytorch/rfcs/pull/30

This defines the aggregation types, the `Stat` class and provides some simple collection of the stats.

This doesn't match the RFC exactly as it incorporates some of the comments on the RFC as well as a few changes for performance.

Changes:
* added window_size to the stats. If specified it will always compute the stat using the `window_size` number of values. If there aren't enough values within that window it reports the previous stats.
* This doesn't include the push metrics yet (will be coming).
  After more discussion it looks like the best way to handle this is to support a hybrid where the metric can set how frequently it'll be logged. For fixed window_size metrics it'll be logged each time it hits the window size. This will allow performant counters as well as lower frequency push counters (window_size=1).

Performance considerations:
* Updating the stats acquires a lock on that Stat object. This should be performant unless there's many-many threads writing to the same stat. Single thread will typically use futex so should be quite fast.
* Adding/removing/fetching all stats sets a global lock on the stat list -- this shouldn't be an issue since these events happen infrequently.
* Fetching stats accesses one stat at a time instead of a global lock. This means the exported values are linearizable but not serializable across multiple stats but I don't expect this to be an issue.

Next steps:
1. Add StatCollector interface for push style metrics
1. Add pybind interfaces to expose to Python
1. Add default metric providers
1. Integrate into Kineto trace view

Test Plan:
buck test //caffe2/test/cpp/monitor:monitor

CI

Reviewed By: kiukchung

Differential Revision: D32266032

fbshipit-source-id: dab8747b4712f5dba5644387817a3a0fda18b66a
2021-11-18 21:46:23 -08:00

198 lines
3.5 KiB
C++

#include <gtest/gtest.h>
#include <torch/csrc/monitor/counters.h>
using namespace torch::monitor;
TEST(MonitorTest, CounterDouble) {
Stat<double> a{
"a",
{MEAN, COUNT},
};
a.add(5.0);
ASSERT_EQ(a.count(), 1);
a.add(6.0);
ASSERT_EQ(a.count(), 2);
a.closeWindow();
auto stats = a.get();
ASSERT_EQ(a.count(), 0);
std::vector<std::pair<Aggregation, double>> want = {
{MEAN, 5.5},
{COUNT, 2.0},
};
ASSERT_EQ(stats, want);
}
TEST(MonitorTest, CounterInt64Sum) {
Stat<int64_t> a{
"a",
{SUM},
};
a.add(5);
a.add(6);
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{SUM, 11},
};
ASSERT_EQ(stats, want);
}
TEST(MonitorTest, CounterInt64Value) {
Stat<int64_t> a{
"a",
{VALUE},
};
a.add(5);
a.add(6);
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{VALUE, 6},
};
ASSERT_EQ(stats, want);
}
TEST(MonitorTest, CounterInt64Mean) {
Stat<int64_t> a{
"a",
{MEAN},
};
a.add(0);
a.add(10);
{
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{MEAN, 5},
};
ASSERT_EQ(stats, want);
}
{
// zero samples case
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{MEAN, 0},
};
ASSERT_EQ(stats, want);
}
}
TEST(MonitorTest, CounterInt64Count) {
Stat<int64_t> a{
"a",
{COUNT},
};
ASSERT_EQ(a.count(), 0);
a.add(0);
ASSERT_EQ(a.count(), 1);
a.add(10);
ASSERT_EQ(a.count(), 2);
a.closeWindow();
auto stats = a.get();
ASSERT_EQ(a.count(), 0);
std::vector<std::pair<Aggregation, int64_t>> want = {
{COUNT, 2},
};
ASSERT_EQ(stats, want);
}
TEST(MonitorTest, CounterInt64MinMax) {
Stat<int64_t> a{
"a",
{MIN, MAX},
};
{
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{MAX, 0},
{MIN, 0},
};
ASSERT_EQ(stats, want);
}
a.add(0);
a.add(5);
a.add(-5);
a.add(-6);
a.add(9);
a.add(2);
{
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{MAX, 9},
{MIN, -6},
};
ASSERT_EQ(stats, want);
}
}
TEST(MonitorTest, CounterInt64WindowSize) {
Stat<int64_t> a{
"a",
{COUNT, SUM},
/*windowSize=*/3,
};
a.add(1);
a.add(2);
ASSERT_EQ(a.count(), 2);
a.add(3);
ASSERT_EQ(a.count(), 0);
a.closeWindow();
auto stats = a.get();
std::vector<std::pair<Aggregation, int64_t>> want = {
{COUNT, 3},
{SUM, 6},
};
ASSERT_EQ(stats, want);
a.closeWindow();
ASSERT_EQ(stats, a.get());
}
TEST(MonitorTest, CloseAndGetStats) {
Stat<int64_t> a{
"a",
{COUNT, SUM},
/*windowSize=*/3,
};
Stat<double> b{
"b",
{MIN, MAX},
2,
};
a.add(1);
b.add(1);
{
auto out = closeAndGetStats();
std::pair<
std::unordered_map<std::string, double>,
std::unordered_map<std::string, int64_t>>
want = {
{{"a.count", 1}, {"a.sum", 1}},
{{"b.min", 0}, {"b.max", 0}},
};
}
a.add(2);
b.add(2);
{
auto out = closeAndGetStats();
std::pair<
std::unordered_map<std::string, double>,
std::unordered_map<std::string, int64_t>>
want = {
{{"a.count", 1}, {"a.sum", 2}},
{{"b.min", 1}, {"b.max", 2}},
};
}
}