199 lines
7.2 KiB
C++
199 lines
7.2 KiB
C++
//===- TrainingLoggerTest.cpp - test for TrainingLogger -------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Analysis/Utils/TrainingLogger.h"
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#include "google/protobuf/struct.pb.h"
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#include "tensorflow/core/example/example.pb.h"
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#include "tensorflow/core/example/feature.pb.h"
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#include "llvm/Analysis/TensorSpec.h"
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#include "llvm/AsmParser/Parser.h"
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#include "llvm/IR/Dominators.h"
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#include "llvm/IR/Instructions.h"
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#include "llvm/IR/LLVMContext.h"
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#include "llvm/IR/Module.h"
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#include "llvm/Support/Path.h"
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#include "llvm/Support/SourceMgr.h"
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#include "llvm/Testing/Support/SupportHelpers.h"
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#include "gtest/gtest.h"
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using namespace llvm;
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extern const char *TestMainArgv0;
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// NOTE! This test model is currently also used by test/Transforms/Inline/ML
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// tests
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//- relevant if updating this model.
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#define PROTO_CHECKER(FNAME, TYPE, INDEX, EXP) \
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do { \
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const auto &V = Expected.feature_lists() \
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.feature_list() \
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.at(FNAME) \
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.feature(INDEX) \
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.TYPE() \
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.value(); \
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for (auto I = 0; I < V.size(); ++I) \
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EXPECT_EQ(V.at(I), EXP[I]); \
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} while (false)
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TEST(TrainingLoggerTest, Logger) {
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std::vector<LoggedFeatureSpec> Features;
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Features.push_back(
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{TensorSpec::createSpec<float>("the_float", {2, 3}), None});
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Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {2}),
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std::string("alternate_name")});
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auto Rewards = TensorSpec::createSpec<float>("reward", {1});
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Logger L(Features, Rewards, true);
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const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
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const int64_t F01[]{2, 3};
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L.logFloatValue(0, F00);
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L.logInt64Value(1, F01);
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L.logFloatReward(3.4);
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const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
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const int64_t F11[]{-2, -3};
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L.logFloatValue(0, F10);
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L.logInt64Value(1, F11);
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L.logFloatReward(-3.0);
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std::string Result;
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raw_string_ostream OS(Result);
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L.flush(OS);
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tensorflow::SequenceExample Expected;
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ASSERT_TRUE(Expected.ParseFromString(Result));
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PROTO_CHECKER("the_float", float_list, 0, F00);
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PROTO_CHECKER("the_float", float_list, 1, F10);
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PROTO_CHECKER("alternate_name", int64_list, 0, F01);
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PROTO_CHECKER("alternate_name", int64_list, 1, F11);
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float R0[]{3.4};
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float R1[]{-3.0};
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PROTO_CHECKER("reward", float_list, 0, R0);
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PROTO_CHECKER("reward", float_list, 1, R1);
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}
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TEST(TrainingLoggerTest, LoggerInt32FeaturesAndReward) {
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std::vector<LoggedFeatureSpec> Features;
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Features.push_back(
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{TensorSpec::createSpec<float>("the_float", {2, 3}), None});
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Features.push_back({TensorSpec::createSpec<int32_t>("the_int", {2}),
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std::string("alternate_name")});
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auto Rewards = TensorSpec::createSpec<int32_t>("reward", {1});
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Logger L(Features, Rewards, true);
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const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
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const int32_t F01[]{2, 3};
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L.logFloatValue(0, F00);
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L.logInt32Value(1, F01);
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L.logInt32Reward(3);
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const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
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const int32_t F11[]{-2, -3};
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L.logFloatValue(0, F10);
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L.logInt32Value(1, F11);
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L.logInt32Reward(-3);
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std::string Result;
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raw_string_ostream OS(Result);
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L.flush(OS);
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tensorflow::SequenceExample Expected;
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ASSERT_TRUE(Expected.ParseFromString(Result));
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PROTO_CHECKER("the_float", float_list, 0, F00);
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PROTO_CHECKER("the_float", float_list, 1, F10);
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PROTO_CHECKER("alternate_name", int64_list, 0, F01);
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PROTO_CHECKER("alternate_name", int64_list, 1, F11);
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int32_t R0[]{3};
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int32_t R1[]{-3};
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PROTO_CHECKER("reward", int64_list, 0, R0);
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PROTO_CHECKER("reward", int64_list, 1, R1);
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}
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TEST(TrainingLoggerTest, LoggerNoReward) {
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std::vector<LoggedFeatureSpec> Features;
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Features.push_back(
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{TensorSpec::createSpec<float>("the_float", {2, 3}), None});
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Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {2}),
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std::string("alternate_name")});
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auto Rewards = TensorSpec::createSpec<float>("reward", {1});
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Logger L(Features, Rewards, false);
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const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
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const int64_t F01[]{2, 3};
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L.logFloatValue(0, F00);
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L.logInt64Value(1, F01);
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const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
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const int64_t F11[]{-2, -3};
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L.logFloatValue(0, F10);
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L.logInt64Value(1, F11);
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std::string Result;
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raw_string_ostream OS(Result);
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L.flush(OS);
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tensorflow::SequenceExample Expected;
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ASSERT_TRUE(Expected.ParseFromString(Result));
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PROTO_CHECKER("the_float", float_list, 0, F00);
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PROTO_CHECKER("the_float", float_list, 1, F10);
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PROTO_CHECKER("alternate_name", int64_list, 0, F01);
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PROTO_CHECKER("alternate_name", int64_list, 1, F11);
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}
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TEST(TrainingLoggerTest, LoggerFinalReward) {
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std::vector<LoggedFeatureSpec> Features;
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Features.push_back({TensorSpec::createSpec<float>("the_float", {1}), None});
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Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {1}), None});
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auto Rewards = TensorSpec::createSpec<float>("reward", {1});
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Logger L(Features, Rewards, true);
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for (int64_t I = 0; I < 3; ++I) {
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float F = static_cast<float>(I);
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L.logFloatValue(0, &F);
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L.logInt64Value(1, &I);
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}
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L.logFloatFinalReward(3.14);
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std::string Result;
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raw_string_ostream OS(Result);
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L.flush(OS);
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const float Zero[]{0.0};
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const float R[]{3.14};
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tensorflow::SequenceExample Expected;
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ASSERT_TRUE(Expected.ParseFromString(Result));
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PROTO_CHECKER("reward", float_list, 0, Zero);
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PROTO_CHECKER("reward", float_list, 1, Zero);
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PROTO_CHECKER("reward", float_list, 2, R);
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}
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TEST(TrainingLoggerTest, LoggerGroup) {
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std::vector<LoggedFeatureSpec> Features;
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Features.push_back({TensorSpec::createSpec<float>("the_float", {1}), None});
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Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {1}), None});
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auto Rewards = TensorSpec::createSpec<float>("reward", {1});
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StringMap<std::unique_ptr<Logger>> Loggers;
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std::vector<std::string> Names{"a", "b"};
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size_t Bump = 0;
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for (auto Name : Names) {
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auto L = std::make_unique<Logger>(Features, Rewards, true);
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for (int64_t I = 0; I < 3; ++I) {
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float F = static_cast<float>(I) + Bump;
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L->logFloatValue(0, &F);
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L->logInt64Value(1, &I);
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}
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L->logFloatFinalReward(3.14 + Bump);
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Loggers.insert(std::make_pair(Name, std::move(L)));
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}
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std::string Result;
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raw_string_ostream OS(Result);
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Logger::flushLogs(OS, Loggers);
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google::protobuf::Struct Expected;
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ASSERT_TRUE(Expected.ParseFromString(Result));
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EXPECT_EQ(Expected.fields_size(), 2);
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EXPECT_TRUE(Expected.fields().contains("a"));
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EXPECT_TRUE(Expected.fields().contains("b"));
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}
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