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504ced25f2
It's generic for the 'development mode', not specific to the inliner case. Differential Revision: https://reviews.llvm.org/D91751
274 lines
8.9 KiB
C++
274 lines
8.9 KiB
C++
//===- TFUtilsTest.cpp - test for TFUtils ---------------------------------===//
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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/TFUtils.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 tests
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//- relevant if updating this model.
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static std::string getModelPath() {
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SmallString<128> InputsDir = unittest::getInputFileDirectory(TestMainArgv0);
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llvm::sys::path::append(InputsDir, "ir2native_x86_64_model");
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return std::string(InputsDir);
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}
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// Test observable behavior when no model is provided.
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TEST(TFUtilsTest, NoModel) {
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TFModelEvaluator Evaluator("", {}, {});
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EXPECT_FALSE(Evaluator.isValid());
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}
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// Test we can correctly load a savedmodel and evaluate it.
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TEST(TFUtilsTest, LoadAndExecuteTest) {
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// We use the ir2native model for test. We know it has one feature of
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// dimension (1, 214)
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const static int64_t KnownSize = 214;
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std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
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"serving_default_input_1", {1, KnownSize})};
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std::vector<TensorSpec> OutputSpecs{
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TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
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TFModelEvaluator Evaluator(getModelPath(), InputSpecs, OutputSpecs);
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EXPECT_TRUE(Evaluator.isValid());
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int32_t *V = Evaluator.getInput<int32_t>(0);
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// Fill it up with 1's, we know the output.
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for (auto I = 0; I < KnownSize; ++I) {
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V[I] = 1;
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}
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{
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auto ER = Evaluator.evaluate();
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EXPECT_TRUE(ER.hasValue());
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float Ret = *ER->getTensorValue<float>(0);
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EXPECT_EQ(static_cast<int64_t>(Ret), 80);
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EXPECT_EQ(ER->getUntypedTensorValue(0),
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reinterpret_cast<const void *>(ER->getTensorValue<float>(0)));
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}
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// The input vector should be unchanged
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for (auto I = 0; I < KnownSize; ++I) {
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EXPECT_EQ(V[I], 1);
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}
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// Zero-out the unused position '0' of the instruction histogram, which is
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// after the first 9 calculated values. Should the the same result.
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V[9] = 0;
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{
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auto ER = Evaluator.evaluate();
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EXPECT_TRUE(ER.hasValue());
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float Ret = *ER->getTensorValue<float>(0);
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EXPECT_EQ(static_cast<int64_t>(Ret), 80);
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}
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}
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// Test incorrect input setup
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TEST(TFUtilsTest, EvalError) {
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// We use the ir2native model for test. We know it has one feature of
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// dimension (1, 214)
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const static int64_t KnownSize = 213;
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std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
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"serving_default_input_1", {1, KnownSize})};
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std::vector<TensorSpec> OutputSpecs{
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TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
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TFModelEvaluator Evaluator(getModelPath(), InputSpecs, OutputSpecs);
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EXPECT_TRUE(Evaluator.isValid());
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int32_t *V = Evaluator.getInput<int32_t>(0);
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// Fill it up with 1's, we know the output.
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for (auto I = 0; I < KnownSize; ++I) {
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V[I] = 1;
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}
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auto ER = Evaluator.evaluate();
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EXPECT_FALSE(ER.hasValue());
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EXPECT_FALSE(Evaluator.isValid());
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}
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TEST(TFUtilsTest, JSONParsing) {
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auto Value = json::parse(
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R"({"name": "tensor_name",
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"port": 2,
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"type": "int32_t",
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"shape":[1,4]
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})");
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EXPECT_TRUE(!!Value);
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LLVMContext Ctx;
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Optional<TensorSpec> Spec = getTensorSpecFromJSON(Ctx, *Value);
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EXPECT_TRUE(Spec.hasValue());
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EXPECT_EQ(*Spec, TensorSpec::createSpec<int32_t>("tensor_name", {1, 4}, 2));
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}
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TEST(TFUtilsTest, JSONParsingInvalidTensorType) {
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auto Value = json::parse(
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R"(
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{"name": "tensor_name",
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"port": 2,
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"type": "no such type",
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"shape":[1,4]
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}
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)");
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EXPECT_TRUE(!!Value);
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LLVMContext Ctx;
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auto Spec = getTensorSpecFromJSON(Ctx, *Value);
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EXPECT_FALSE(Spec.hasValue());
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}
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TEST(TFUtilsTest, TensorSpecSizesAndTypes) {
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auto Spec1D = TensorSpec::createSpec<int16_t>("Hi1", {1});
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auto Spec2D = TensorSpec::createSpec<int16_t>("Hi2", {1, 1});
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auto Spec1DLarge = TensorSpec::createSpec<float>("Hi3", {10});
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auto Spec3DLarge = TensorSpec::createSpec<float>("Hi3", {2, 4, 10});
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EXPECT_TRUE(Spec1D.isElementType<int16_t>());
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EXPECT_FALSE(Spec3DLarge.isElementType<double>());
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EXPECT_EQ(Spec1D.getElementCount(), 1U);
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EXPECT_EQ(Spec2D.getElementCount(), 1U);
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EXPECT_EQ(Spec1DLarge.getElementCount(), 10U);
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EXPECT_EQ(Spec3DLarge.getElementCount(), 80U);
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EXPECT_EQ(Spec3DLarge.getElementByteSize(), sizeof(float));
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EXPECT_EQ(Spec1D.getElementByteSize(), sizeof(int16_t));
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}
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TEST(TFUtilsTest, 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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float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
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int64_t F01[]{2, 3};
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L.logTensorValue(0, F00, 6);
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L.logTensorValue(1, F01, 2);
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L.logReward<float>(3.4);
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float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
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int64_t F11[]{-2, -3};
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L.logTensorValue(0, F10, 6);
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L.logTensorValue(1, F11, 2);
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L.logReward<float>(-3.0);
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const auto *Expected = R"(feature_lists: {
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feature_list: {
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key: "the_float" value: {
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feature: { float_list: { value: [0.000000e+00, 1.000000e-01, 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01] } }
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feature: { float_list: { value: [0.000000e+00, 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00] } }
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}
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}
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feature_list: {
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key: "alternate_name" value: {
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feature: { int64_list: { value: [2, 3] } }
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feature: { int64_list: { value: [-2, -3] } }
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}
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}
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feature_list: {
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key: "reward" value: {
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feature: { float_list: { value: [3.400000e+00] } }
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feature: { float_list: { value: [-3.000000e+00] } }
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}
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}
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}
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)";
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std::string Result;
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raw_string_ostream OS(Result);
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L.print(OS);
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EXPECT_EQ(Result, Expected);
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}
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TEST(TFUtilsTest, 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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float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
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int64_t F01[]{2, 3};
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L.logTensorValue(0, F00, 6);
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L.logTensorValue(1, F01, 2);
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float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
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int64_t F11[]{-2, -3};
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L.logTensorValue(0, F10, 6);
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L.logTensorValue(1, F11, 2);
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const auto *Expected = R"(feature_lists: {
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feature_list: {
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key: "the_float" value: {
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feature: { float_list: { value: [0.000000e+00, 1.000000e-01, 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01] } }
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feature: { float_list: { value: [0.000000e+00, 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00] } }
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}
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}
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feature_list: {
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key: "alternate_name" value: {
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feature: { int64_list: { value: [2, 3] } }
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feature: { int64_list: { value: [-2, -3] } }
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}
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}
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}
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)";
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std::string Result;
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raw_string_ostream OS(Result);
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L.print(OS);
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EXPECT_EQ(Result, Expected);
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}
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TEST(TFUtilsTest, 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 (size_t I = 0; I < 3; ++I) {
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float F = static_cast<float>(I);
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L.logTensorValue(0, &F);
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L.logTensorValue(1, &I);
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}
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L.logFinalReward<float>(3.14);
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const auto *Expected = R"(feature_lists: {
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feature_list: {
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key: "the_float" value: {
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feature: { float_list: { value: [0.000000e+00] } }
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feature: { float_list: { value: [1.000000e+00] } }
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feature: { float_list: { value: [2.000000e+00] } }
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}
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}
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feature_list: {
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key: "the_int" value: {
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feature: { int64_list: { value: [0] } }
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feature: { int64_list: { value: [1] } }
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feature: { int64_list: { value: [2] } }
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}
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}
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feature_list: {
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key: "reward" value: {
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feature: { float_list: { value: [0.000000e+00] } }
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feature: { float_list: { value: [0.000000e+00] } }
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feature: { float_list: { value: [3.140000e+00] } }
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}
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}
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}
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)";
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std::string Result;
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raw_string_ostream OS(Result);
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L.print(OS);
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EXPECT_EQ(Result, Expected);
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}
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