text: //===- TFUtilsTest.cpp - test for TFUtils ---------------------------------===//
  //
  // 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 "llvm/Analysis/Utils/TFUtils.h"
  #include "llvm/AsmParser/Parser.h"
  #include "llvm/IR/Dominators.h"
  #include "llvm/IR/Instructions.h"
  #include "llvm/IR/LLVMContext.h"
  #include "llvm/IR/Module.h"
  #include "llvm/Support/Path.h"
  #include "llvm/Support/SourceMgr.h"
  #include "llvm/Testing/Support/SupportHelpers.h"
  #include "gtest/gtest.h"
  
  using namespace llvm;
  
  extern const char *TestMainArgv0;
  
  // NOTE! This test model is currently also used by test/Transforms/Inline/ML tests
  //- relevant if updating this model.
  static std::string getModelPath() {
    SmallString<128> InputsDir = unittest::getInputFileDirectory(TestMainArgv0);
    llvm::sys::path::append(InputsDir, "ir2native_x86_64_model");
    return std::string(InputsDir);
  }
  
  // Test observable behavior when no model is provided.
  TEST(TFUtilsTest, NoModel) {
    TFModelEvaluator Evaluator("", {}, {});
    EXPECT_FALSE(Evaluator.isValid());
  }
  
  // Test we can correctly load a savedmodel and evaluate it.
  TEST(TFUtilsTest, LoadAndExecuteTest) {
    // We use the ir2native model for test. We know it has one feature of
    // dimension (1, 214)
    const static int64_t KnownSize = 214;
    std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
        "serving_default_input_1", {1, KnownSize})};
    std::vector<TensorSpec> OutputSpecs{
        TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
  
    TFModelEvaluator Evaluator(getModelPath(), InputSpecs, OutputSpecs);
    EXPECT_TRUE(Evaluator.isValid());
  
    int32_t *V = Evaluator.getInput<int32_t>(0);
    // Fill it up with 1's, we know the output.
    for (auto I = 0; I < KnownSize; ++I) {
      V[I] = 1;
    }
    {
      auto ER = Evaluator.evaluate();
      EXPECT_TRUE(ER.hasValue());
      float Ret = *ER->getTensorValue<float>(0);
      EXPECT_EQ(static_cast<int64_t>(Ret), 80);
      EXPECT_EQ(ER->getUntypedTensorValue(0),
                reinterpret_cast<const void *>(ER->getTensorValue<float>(0)));
    }
    // The input vector should be unchanged
    for (auto I = 0; I < KnownSize; ++I) {
      EXPECT_EQ(V[I], 1);
    }
    // Zero-out the unused position '0' of the instruction histogram, which is
    // after the first 9 calculated values. Should the the same result.
    V[9] = 0;
    {
      auto ER = Evaluator.evaluate();
      EXPECT_TRUE(ER.hasValue());
      float Ret = *ER->getTensorValue<float>(0);
      EXPECT_EQ(static_cast<int64_t>(Ret), 80);
    }
  }
  
  // Test incorrect input setup
  TEST(TFUtilsTest, EvalError) {
    // We use the ir2native model for test. We know it has one feature of
    // dimension (1, 214)
    const static int64_t KnownSize = 213;
    std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
        "serving_default_input_1", {1, KnownSize})};
    std::vector<TensorSpec> OutputSpecs{
        TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
  
    TFModelEvaluator Evaluator(getModelPath(), InputSpecs, OutputSpecs);
    EXPECT_TRUE(Evaluator.isValid());
  
    int32_t *V = Evaluator.getInput<int32_t>(0);
    // Fill it up with 1's, we know the output.
    for (auto I = 0; I < KnownSize; ++I) {
      V[I] = 1;
    }
    auto ER = Evaluator.evaluate();
    EXPECT_FALSE(ER.hasValue());
    EXPECT_FALSE(Evaluator.isValid());
  }
  
  TEST(TFUtilsTest, JSONParsing) {
    auto Value = json::parse(
        R"({"name": "tensor_name", 
          "port": 2, 
          "type": "int32_t", 
          "shape":[1,4]
          })");
    EXPECT_TRUE(!!Value);
    LLVMContext Ctx;
    Optional<TensorSpec> Spec = getTensorSpecFromJSON(Ctx, *Value);
    EXPECT_TRUE(Spec.hasValue());
    EXPECT_EQ(*Spec, TensorSpec::createSpec<int32_t>("tensor_name", {1, 4}, 2));
  }
  
  TEST(TFUtilsTest, JSONParsingInvalidTensorType) {
    auto Value = json::parse(
        R"(
          {"name": "tensor_name", 
          "port": 2, 
          "type": "no such type", 
          "shape":[1,4]
          }
        )");
    EXPECT_TRUE(!!Value);
    LLVMContext Ctx;
    auto Spec = getTensorSpecFromJSON(Ctx, *Value);
    EXPECT_FALSE(Spec.hasValue());
  }
  
  TEST(TFUtilsTest, TensorSpecSizesAndTypes) {
    auto Spec1D = TensorSpec::createSpec<int16_t>("Hi1", {1});
    auto Spec2D = TensorSpec::createSpec<int16_t>("Hi2", {1, 1});
    auto Spec1DLarge = TensorSpec::createSpec<float>("Hi3", {10});
    auto Spec3DLarge = TensorSpec::createSpec<float>("Hi3", {2, 4, 10});
    EXPECT_TRUE(Spec1D.isElementType<int16_t>());
    EXPECT_FALSE(Spec3DLarge.isElementType<double>());
    EXPECT_EQ(Spec1D.getElementCount(), 1U);
    EXPECT_EQ(Spec2D.getElementCount(), 1U);
    EXPECT_EQ(Spec1DLarge.getElementCount(), 10U);
    EXPECT_EQ(Spec3DLarge.getElementCount(), 80U);
    EXPECT_EQ(Spec3DLarge.getElementByteSize(), sizeof(float));
    EXPECT_EQ(Spec1D.getElementByteSize(), sizeof(int16_t));
  }
  
  TEST(TFUtilsTest, Logger) {
    std::vector<Logger::LoggedFeatureSpec> Features;
    Features.push_back(
        {TensorSpec::createSpec<float>("the_float", {2, 3}), None});
    Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {2}),
                        std::string("alternate_name")});
  
    auto Rewards = TensorSpec::createSpec<float>("reward", {1});
    Logger L(Features, Rewards, true);
    float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
    int64_t F01[]{2, 3};
  
    L.logTensorValue(0, F00, 6);
    L.logTensorValue(1, F01, 2);
    L.logReward<float>(3.4);
    float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
    int64_t F11[]{-2, -3};
    L.logTensorValue(0, F10, 6);
    L.logTensorValue(1, F11, 2);
    L.logReward<float>(-3.0);
    const auto *Expected = R"(feature_lists: {
    feature_list: {
      key: "the_float" value: {
        feature: { float_list: { value: [0.000000e+00, 1.000000e-01, 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01] } }
        feature: { float_list: { value: [0.000000e+00, 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00] } }
      }
    }
    feature_list: {
      key: "alternate_name" value: {
        feature: { int64_list: { value: [2, 3] } }
        feature: { int64_list: { value: [-2, -3] } }
      }
    }
    feature_list: {
      key: "reward" value: {
        feature: { float_list: { value: [3.400000e+00] } }
        feature: { float_list: { value: [-3.000000e+00] } }
      }
    }
  }
  )";
    std::string Result;
    raw_string_ostream OS(Result);
    L.print(OS);
    EXPECT_EQ(Result, Expected);
  }
  
  TEST(TFUtilsTest, LoggerNoReward) {
    std::vector<Logger::LoggedFeatureSpec> Features;
    Features.push_back(
        {TensorSpec::createSpec<float>("the_float", {2, 3}), None});
    Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {2}),
                        std::string("alternate_name")});
  
    auto Rewards = TensorSpec::createSpec<float>("reward", {1});
    Logger L(Features, Rewards, false);
    float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
    int64_t F01[]{2, 3};
  
    L.logTensorValue(0, F00, 6);
    L.logTensorValue(1, F01, 2);
    float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
    int64_t F11[]{-2, -3};
    L.logTensorValue(0, F10, 6);
    L.logTensorValue(1, F11, 2);
    const auto *Expected = R"(feature_lists: {
    feature_list: {
      key: "the_float" value: {
        feature: { float_list: { value: [0.000000e+00, 1.000000e-01, 2.000000e-01, 3.000000e-01, 4.000000e-01, 5.000000e-01] } }
        feature: { float_list: { value: [0.000000e+00, 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00] } }
      }
    }
    feature_list: {
      key: "alternate_name" value: {
        feature: { int64_list: { value: [2, 3] } }
        feature: { int64_list: { value: [-2, -3] } }
      }
    }
  }
  )";
    std::string Result;
    raw_string_ostream OS(Result);
    L.print(OS);
    EXPECT_EQ(Result, Expected);
  }