experiments/STT-tensorflow
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[data-stats] Adds support for gathering statistics as metrics with stats_aggreagtor.

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Also collects metrics for `examples_count`, `features_count`, `feature_values_count`, `feature_lists_count` and `sequence_examples_count`  when `feature_stats()` transformation is applied to the dataset.

PiperOrigin-RevId: 202667632
This commit is contained in:
Shivani Agrawal 2018-06-29 10:27:16 -07:00 committed by TensorFlower Gardener
parent 34df1eea29
commit f139bc3f5c
3 changed files with 45 additions and 1 deletions
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4
tensorflow/core/framework/stats_aggregator.h
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@ -57,6 +57,10 @@ class StatsAggregator {
// interface. It is possible that not all implementations will support
// encoding their state as a protocol buffer.
virtual void EncodeToProto(Summary* out_summary) = 0;
// Increment the `label` cell of metrics mapped with `name` by given `value`.
virtual void IncrementCounter(const string& name, const string& label,
int64 val) = 0;
};
// A `StatsAggregatorResource` wraps a shareable `StatsAggregator` as a resource

29
tensorflow/core/kernels/data/stats_aggregator_ops.cc
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@ -20,11 +20,25 @@ limitations under the License.
#include "tensorflow/core/framework/resource_op_kernel.h"
#include "tensorflow/core/framework/summary.pb.h"
#include "tensorflow/core/lib/histogram/histogram.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/gauge.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/macros.h"
namespace tensorflow {
namespace {
static mutex* get_counters_map_lock() {
static mutex counters_map_lock(LINKER_INITIALIZED);
return &counters_map_lock;
}
static std::unordered_map<string, monitoring::Counter<1>*>* get_counters_map() {
static std::unordered_map<string, monitoring::Counter<1>*>* counters_map =
new std::unordered_map<string, monitoring::Counter<1>*>;
return counters_map;
}
class StatsAggregatorImpl : public StatsAggregator {
public:
StatsAggregatorImpl() {}
@ -61,6 +75,21 @@ class StatsAggregatorImpl : public StatsAggregator {
}
}
void IncrementCounter(const string& name, const string& label,
int64 val) override {
mutex_lock l(*get_counters_map_lock());
auto counters_map = get_counters_map();
if (counters_map->find(name) == counters_map->end()) {
counters_map->emplace(
name, monitoring::Counter<1>::New(
/*streamz name*/ "/tensorflow/" + name,
/*streamz description*/
name + " generated or consumed by the component.",
/*streamz label name*/ "component_descriptor"));
}
counters_map->at(name)->GetCell(label)->IncrementBy(val);
}
private:
mutex mu_;
std::unordered_map<string, histogram::Histogram> histograms_ GUARDED_BY(mu_);

13
tensorflow/core/kernels/data/stats_dataset_ops.cc
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@ -316,10 +316,14 @@ class FeatureStatsDatasetOp : public UnaryDatasetOpKernel {
// changes to parse_example() where it returns stats as well.
for (int i = 0; i < record_t.size(); ++i) {
if (example.ParseFromString(record_t(i))) {
stats_aggregator->IncrementCounter("examples_count", "trainer",
1);
AddStatsFeatures(example, stats_aggregator);
} else {
SequenceExample sequence_example;
if (sequence_example.ParseFromString(record_t(i))) {
stats_aggregator->IncrementCounter("sequence_examples_count",
"trainer", 1);
AddStatsFeatures(sequence_example, stats_aggregator);
}
}
@ -360,8 +364,11 @@ class FeatureStatsDatasetOp : public UnaryDatasetOpKernel {
int feature_values_list_size_sum = 0;
for (const auto& feature : example.features().feature()) {
stats_aggregator->IncrementCounter("features_count", "trainer", 1);
feature_values_list_size_sum += AddStatsFeatureValues(feature.second);
}
stats_aggregator->IncrementCounter("feature_values_count", "trainer",
feature_values_list_size_sum);
stats_aggregator->AddToHistogram(
strings::StrCat(dataset()->tag_, ":feature-values"),
{static_cast<double>(feature_values_list_size_sum)});
@ -378,16 +385,20 @@ class FeatureStatsDatasetOp : public UnaryDatasetOpKernel {
int feature_values_list_size_sum = 0;
for (const auto& feature : example.context().feature()) {
stats_aggregator->IncrementCounter("features_count", "trainer", 1);
feature_values_list_size_sum += AddStatsFeatureValues(feature.second);
}
for (const auto& feature_list :
example.feature_lists().feature_list()) {
stats_aggregator->IncrementCounter("feature_lists_count", "reainer",
1);
for (const auto& feature : feature_list.second.feature()) {
feature_values_list_size_sum += AddStatsFeatureValues(feature);
}
}
stats_aggregator->IncrementCounter("feature_values_count", "trainer",
feature_values_list_size_sum);
stats_aggregator->AddToHistogram(
strings::StrCat(dataset()->tag_, ":feature-values"),
{static_cast<double>(feature_values_list_size_sum)});