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//===- 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 "google/protobuf/struct.pb.h"
#include "tensorflow/core/example/example.pb.h"
#include "tensorflow/core/example/feature.pb.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));
}
#define PROTO_CHECKER(FNAME, TYPE, INDEX, EXP) \
do { \
const auto &V = Expected.feature_lists() \
.feature_list() \
.at(FNAME) \
.feature(INDEX) \
.TYPE() \
.value(); \
for (auto I = 0; I < V.size(); ++I) \
EXPECT_EQ(V.at(I), EXP[I]); \
} while (false)
TEST(TFUtilsTest, Logger) {
std::vector<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);
const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
const int64_t F01[]{2, 3};
L.logFloatValue(0, F00);
L.logInt64Value(1, F01);
L.logFloatReward(3.4);
const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
const int64_t F11[]{-2, -3};
L.logFloatValue(0, F10);
L.logInt64Value(1, F11);
L.logFloatReward(-3.0);
std::string Result;
raw_string_ostream OS(Result);
L.flush(OS);
tensorflow::SequenceExample Expected;
ASSERT_TRUE(Expected.ParseFromString(Result));
PROTO_CHECKER("the_float", float_list, 0, F00);
PROTO_CHECKER("the_float", float_list, 1, F10);
PROTO_CHECKER("alternate_name", int64_list, 0, F01);
PROTO_CHECKER("alternate_name", int64_list, 1, F11);
float R0[]{3.4};
float R1[]{-3.0};
PROTO_CHECKER("reward", float_list, 0, R0);
PROTO_CHECKER("reward", float_list, 1, R1);
}
TEST(TFUtilsTest, LoggerInt32FeaturesAndReward) {
std::vector<LoggedFeatureSpec> Features;
Features.push_back(
{TensorSpec::createSpec<float>("the_float", {2, 3}), None});
Features.push_back({TensorSpec::createSpec<int32_t>("the_int", {2}),
std::string("alternate_name")});
auto Rewards = TensorSpec::createSpec<int32_t>("reward", {1});
Logger L(Features, Rewards, true);
const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
const int32_t F01[]{2, 3};
L.logFloatValue(0, F00);
L.logInt32Value(1, F01);
L.logInt32Reward(3);
const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
const int32_t F11[]{-2, -3};
L.logFloatValue(0, F10);
L.logInt32Value(1, F11);
L.logInt32Reward(-3);
std::string Result;
raw_string_ostream OS(Result);
L.flush(OS);
tensorflow::SequenceExample Expected;
ASSERT_TRUE(Expected.ParseFromString(Result));
PROTO_CHECKER("the_float", float_list, 0, F00);
PROTO_CHECKER("the_float", float_list, 1, F10);
PROTO_CHECKER("alternate_name", int64_list, 0, F01);
PROTO_CHECKER("alternate_name", int64_list, 1, F11);
int32_t R0[]{3};
int32_t R1[]{-3};
PROTO_CHECKER("reward", int64_list, 0, R0);
PROTO_CHECKER("reward", int64_list, 1, R1);
}
TEST(TFUtilsTest, LoggerNoReward) {
std::vector<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);
const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
const int64_t F01[]{2, 3};
L.logFloatValue(0, F00);
L.logInt64Value(1, F01);
const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
const int64_t F11[]{-2, -3};
L.logFloatValue(0, F10);
L.logInt64Value(1, F11);
std::string Result;
raw_string_ostream OS(Result);
L.flush(OS);
tensorflow::SequenceExample Expected;
ASSERT_TRUE(Expected.ParseFromString(Result));
PROTO_CHECKER("the_float", float_list, 0, F00);
PROTO_CHECKER("the_float", float_list, 1, F10);
PROTO_CHECKER("alternate_name", int64_list, 0, F01);
PROTO_CHECKER("alternate_name", int64_list, 1, F11);
}
TEST(TFUtilsTest, LoggerFinalReward) {
std::vector<LoggedFeatureSpec> Features;
Features.push_back({TensorSpec::createSpec<float>("the_float", {1}), None});
Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {1}), None});
auto Rewards = TensorSpec::createSpec<float>("reward", {1});
Logger L(Features, Rewards, true);
for (int64_t I = 0; I < 3; ++I) {
float F = static_cast<float>(I);
L.logFloatValue(0, &F);
L.logInt64Value(1, &I);
}
L.logFloatFinalReward(3.14);
std::string Result;
raw_string_ostream OS(Result);
L.flush(OS);
const float Zero[]{0.0};
const float R[]{3.14};
tensorflow::SequenceExample Expected;
ASSERT_TRUE(Expected.ParseFromString(Result));
PROTO_CHECKER("reward", float_list, 0, Zero);
PROTO_CHECKER("reward", float_list, 1, Zero);
PROTO_CHECKER("reward", float_list, 2, R);
}
TEST(TFUtilsTest, LoggerGroup) {
std::vector<LoggedFeatureSpec> Features;
Features.push_back({TensorSpec::createSpec<float>("the_float", {1}), None});
Features.push_back({TensorSpec::createSpec<int64_t>("the_int", {1}), None});
auto Rewards = TensorSpec::createSpec<float>("reward", {1});
StringMap<std::unique_ptr<Logger>> Loggers;
std::vector<std::string> Names{"a", "b"};
size_t Bump = 0;
for (auto Name : Names) {
auto L = std::make_unique<Logger>(Features, Rewards, true);
for (int64_t I = 0; I < 3; ++I) {
float F = static_cast<float>(I) + Bump;
L->logFloatValue(0, &F);
L->logInt64Value(1, &I);
}
L->logFloatFinalReward(3.14 + Bump);
Loggers.insert(std::make_pair(Name, std::move(L)));
}
std::string Result;
raw_string_ostream OS(Result);
Logger::flushLogs(OS, Loggers);
google::protobuf::Struct Expected;
ASSERT_TRUE(Expected.ParseFromString(Result));
EXPECT_EQ(Expected.fields_size(), 2);
EXPECT_TRUE(Expected.fields().contains("a"));
EXPECT_TRUE(Expected.fields().contains("b"));
}
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