libc/benchmarks/automemcpy/unittests/ResultAnalyzerTest.cpp

//===--  Automemcpy Json Results Analyzer Test ----------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#include "automemcpy/ResultAnalyzer.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"

using testing::ElementsAre;
using testing::Pair;
using testing::SizeIs;

namespace llvm {
namespace automemcpy {
namespace {

TEST(AutomemcpyJsonResultsAnalyzer, getThroughputsOneSample) {
  static constexpr FunctionId Foo1 = {"memcpy1", FunctionType::MEMCPY};
  static constexpr DistributionId DistA = {{"A"}};
  static constexpr SampleId Id = {Foo1, DistA};
  static constexpr Sample kSamples[] = {
      Sample{Id, 4},
  };

  const std::vector<FunctionData> Data = getThroughputs(kSamples);
  EXPECT_THAT(Data, SizeIs(1));
  EXPECT_THAT(Data[0].Id, Foo1);
  EXPECT_THAT(Data[0].PerDistributionData, SizeIs(1));
  // A single value is provided.
  EXPECT_THAT(
      Data[0].PerDistributionData.lookup(DistA.Name).MedianBytesPerSecond, 4);
}

TEST(AutomemcpyJsonResultsAnalyzer, getThroughputsManySamplesSameBucket) {
  static constexpr FunctionId Foo1 = {"memcpy1", FunctionType::MEMCPY};
  static constexpr DistributionId DistA = {{"A"}};
  static constexpr SampleId Id = {Foo1, DistA};
  static constexpr Sample kSamples[] = {Sample{Id, 4}, Sample{Id, 5},
                                        Sample{Id, 5}};

  const std::vector<FunctionData> Data = getThroughputs(kSamples);
  EXPECT_THAT(Data, SizeIs(1));
  EXPECT_THAT(Data[0].Id, Foo1);
  EXPECT_THAT(Data[0].PerDistributionData, SizeIs(1));
  // When multiple values are provided we pick the median one (here median of 4,
  // 5, 5).
  EXPECT_THAT(
      Data[0].PerDistributionData.lookup(DistA.Name).MedianBytesPerSecond, 5);
}

TEST(AutomemcpyJsonResultsAnalyzer, getThroughputsServeralFunctionAndDist) {
  static constexpr FunctionId Foo1 = {"memcpy1", FunctionType::MEMCPY};
  static constexpr DistributionId DistA = {{"A"}};
  static constexpr FunctionId Foo2 = {"memcpy2", FunctionType::MEMCPY};
  static constexpr DistributionId DistB = {{"B"}};
  static constexpr Sample kSamples[] = {
      Sample{{Foo1, DistA}, 1}, Sample{{Foo1, DistB}, 2},
      Sample{{Foo2, DistA}, 3}, Sample{{Foo2, DistB}, 4}};
  // Data is aggregated per function.
  const std::vector<FunctionData> Data = getThroughputs(kSamples);
  EXPECT_THAT(Data, SizeIs(2)); // 2 functions Foo1 and Foo2.
  // Each function has data for both distributions DistA and DistB.
  EXPECT_THAT(Data[0].PerDistributionData, SizeIs(2));
  EXPECT_THAT(Data[1].PerDistributionData, SizeIs(2));
}

TEST(AutomemcpyJsonResultsAnalyzer, getScore) {
  static constexpr FunctionId Foo1 = {"memcpy1", FunctionType::MEMCPY};
  static constexpr FunctionId Foo2 = {"memcpy2", FunctionType::MEMCPY};
  static constexpr FunctionId Foo3 = {"memcpy3", FunctionType::MEMCPY};
  static constexpr DistributionId Dist = {{"A"}};
  static constexpr Sample kSamples[] = {Sample{{Foo1, Dist}, 1},
                                        Sample{{Foo2, Dist}, 2},
                                        Sample{{Foo3, Dist}, 3}};

  // Data is aggregated per function.
  std::vector<FunctionData> Data = getThroughputs(kSamples);

  // Sort Data by function name so we can test them.
  std::sort(
      Data.begin(), Data.end(),
      [](const FunctionData &A, const FunctionData &B) { return A.Id < B.Id; });

  EXPECT_THAT(Data[0].Id, Foo1);
  EXPECT_THAT(Data[0].PerDistributionData.lookup("A").MedianBytesPerSecond, 1);
  EXPECT_THAT(Data[1].Id, Foo2);
  EXPECT_THAT(Data[1].PerDistributionData.lookup("A").MedianBytesPerSecond, 2);
  EXPECT_THAT(Data[2].Id, Foo3);
  EXPECT_THAT(Data[2].PerDistributionData.lookup("A").MedianBytesPerSecond, 3);

  // Normalizes throughput per distribution.
  fillScores(Data);
  EXPECT_THAT(Data[0].PerDistributionData.lookup("A").Score, 0);
  EXPECT_THAT(Data[1].PerDistributionData.lookup("A").Score, 0.5);
  EXPECT_THAT(Data[2].PerDistributionData.lookup("A").Score, 1);
}

TEST(AutomemcpyJsonResultsAnalyzer, castVotes) {
  static constexpr double kAbsErr = 0.01;

  static constexpr FunctionId Foo1 = {"memcpy1", FunctionType::MEMCPY};
  static constexpr FunctionId Foo2 = {"memcpy2", FunctionType::MEMCPY};
  static constexpr FunctionId Foo3 = {"memcpy3", FunctionType::MEMCPY};
  static constexpr DistributionId DistA = {{"A"}};
  static constexpr DistributionId DistB = {{"B"}};
  static constexpr Sample kSamples[] = {
      Sample{{Foo1, DistA}, 0}, Sample{{Foo1, DistB}, 30},
      Sample{{Foo2, DistA}, 1}, Sample{{Foo2, DistB}, 100},
      Sample{{Foo3, DistA}, 7}, Sample{{Foo3, DistB}, 100},
  };

  // DistA Thoughput ranges from 0 to 7.
  // DistB Thoughput ranges from 30 to 100.

  // Data is aggregated per function.
  std::vector<FunctionData> Data = getThroughputs(kSamples);

  // Sort Data by function name so we can test them.
  std::sort(
      Data.begin(), Data.end(),
      [](const FunctionData &A, const FunctionData &B) { return A.Id < B.Id; });

  // Normalizes throughput per distribution.
  fillScores(Data);

  // Cast votes
  castVotes(Data);

  EXPECT_THAT(Data[0].Id, Foo1);
  EXPECT_THAT(Data[1].Id, Foo2);
  EXPECT_THAT(Data[2].Id, Foo3);

  // Distribution A
  // Throughput is 0, 1 and 7, so normalized scores are 0, 1/7 and 1.
  EXPECT_NEAR(Data[0].PerDistributionData.lookup("A").Score, 0, kAbsErr);
  EXPECT_NEAR(Data[1].PerDistributionData.lookup("A").Score, 1. / 7, kAbsErr);
  EXPECT_NEAR(Data[2].PerDistributionData.lookup("A").Score, 1, kAbsErr);
  // which are turned into grades BAD,  MEDIOCRE and EXCELLENT.
  EXPECT_THAT(Data[0].PerDistributionData.lookup("A").Grade, Grade::BAD);
  EXPECT_THAT(Data[1].PerDistributionData.lookup("A").Grade, Grade::MEDIOCRE);
  EXPECT_THAT(Data[2].PerDistributionData.lookup("A").Grade, Grade::EXCELLENT);

  // Distribution B
  // Throughput is 30, 100 and 100, so normalized scores are 0, 1 and 1.
  EXPECT_NEAR(Data[0].PerDistributionData.lookup("B").Score, 0, kAbsErr);
  EXPECT_NEAR(Data[1].PerDistributionData.lookup("B").Score, 1, kAbsErr);
  EXPECT_NEAR(Data[2].PerDistributionData.lookup("B").Score, 1, kAbsErr);
  // which are turned into grades BAD, EXCELLENT and EXCELLENT.
  EXPECT_THAT(Data[0].PerDistributionData.lookup("B").Grade, Grade::BAD);
  EXPECT_THAT(Data[1].PerDistributionData.lookup("B").Grade, Grade::EXCELLENT);
  EXPECT_THAT(Data[2].PerDistributionData.lookup("B").Grade, Grade::EXCELLENT);

  // Now looking from the functions point of view.
  // Note the array is indexed by GradeEnum values (EXCELLENT=0 / BAD = 6)
  EXPECT_THAT(Data[0].GradeHisto, ElementsAre(0, 0, 0, 0, 0, 0, 2));
  EXPECT_THAT(Data[1].GradeHisto, ElementsAre(1, 0, 0, 0, 0, 1, 0));
  EXPECT_THAT(Data[2].GradeHisto, ElementsAre(2, 0, 0, 0, 0, 0, 0));

  EXPECT_THAT(Data[0].FinalGrade, Grade::BAD);
  EXPECT_THAT(Data[1].FinalGrade, Grade::MEDIOCRE);
  EXPECT_THAT(Data[2].FinalGrade, Grade::EXCELLENT);
}

} // namespace
} // namespace automemcpy
} // namespace llvm