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ensemble_flow_custom_node_tests.cpp
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//*****************************************************************************
// Copyright 2021 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <array>
#include <functional>
#include <limits>
#include <numeric>
#include <optional>
#include <regex>
#include <string>
#include <utility>
#pragma warning(push)
#pragma warning(disable : 4624)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wall"
#pragma GCC diagnostic ignored "-Wunused-but-set-variable"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow_serving/apis/prediction_service.grpc.pb.h"
#pragma GCC diagnostic pop
#pragma warning(pop)
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "../dags/custom_node.hpp"
#include "../dags/custom_node_library_manager.hpp"
#include "../dags/dl_node.hpp"
#include "../dags/entry_node.hpp"
#include "../dags/exit_node.hpp"
#include "../dags/node_library.hpp"
#include "../dags/node_library_utils.hpp"
#include "../dags/nodestreamidguard.hpp"
#include "../dags/pipeline.hpp"
#include "../dags/pipelinedefinition.hpp"
#include "../execution_context.hpp"
#include "../metric_registry.hpp"
#include "../model_metric_reporter.hpp"
#include "../modelinstance.hpp"
#include "../modelinstanceunloadguard.hpp"
#include "../precision.hpp"
#include "../stringutils.hpp"
#include "test_utils.hpp"
using namespace ovms;
using tensorflow::serving::PredictRequest;
using tensorflow::serving::PredictResponse;
class EnsembleFlowCustomNodePipelineExecutionTest : public TestWithTempDir {
protected:
void SetUp() override {
TestWithTempDir::SetUp();
reporter = std::make_unique<ModelMetricReporter>(&this->metricConfig, &this->registry, "example_pipeline_name", 1);
CustomNodeLibraryManager manager;
ASSERT_EQ(manager.loadLibrary(
this->libraryName,
getGenericFullPathForBazelOut(this->libraryPath)),
StatusCode::OK);
ASSERT_EQ(manager.getLibrary(
this->libraryName,
this->library),
StatusCode::OK);
dagDummyModelOutputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
DUMMY_MODEL_SHAPE_META,
Layout{"NC"});
dagDummyModelInputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
DUMMY_MODEL_SHAPE_META,
Layout{"NC"});
}
template <typename T>
void prepareRequest(const std::vector<T>& data) {
this->prepareRequest(this->request, data);
}
template <typename T>
void prepareRequest(PredictRequest& request, const std::vector<T>& data, const std::string& inputName = pipelineInputName, const shape_t shape = {}) {
tensorflow::TensorProto& proto = (*request.mutable_inputs())[inputName];
proto.set_dtype(tensorflow::DataTypeToEnum<T>::value);
proto.mutable_tensor_content()->assign((char*)data.data(), data.size() * sizeof(T));
if (shape.size()) {
for (auto& dim : shape) {
proto.mutable_tensor_shape()->add_dim()->set_size(dim);
}
} else {
proto.mutable_tensor_shape()->add_dim()->set_size(1);
proto.mutable_tensor_shape()->add_dim()->set_size(data.size());
}
}
template <typename T>
std::unique_ptr<Pipeline> prepareSingleNodePipelineWithLibraryMock() {
const std::vector<float> inputValues{3.5, 2.1, -0.2};
auto inputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t inputsInfo{{pipelineInputName, inputTensorInfo}};
this->prepareRequest(inputValues);
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, dagDummyModelOutputTensorInfo}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto custom_node = std::make_unique<CustomNode>(
customNodeName,
createLibraryMock<T>(),
parameters_t{});
auto pipeline = std::make_unique<Pipeline>(*input_node, *output_node, *this->reporter);
pipeline->connect(*input_node, *custom_node, {{pipelineInputName, customNodeInputName}});
pipeline->connect(*custom_node, *output_node, {{customNodeOutputName, pipelineOutputName}});
pipeline->push(std::move(input_node));
pipeline->push(std::move(custom_node));
pipeline->push(std::move(output_node));
return pipeline;
}
template <typename T>
void checkResponse(std::vector<T> data, std::function<T(T)> op) {
this->checkResponse(this->pipelineOutputName, data, op);
}
template <typename T>
void checkResponse(const std::string& outputName, std::vector<T> data, std::function<T(T)> op) {
this->checkResponse(outputName, this->response, data, op);
}
template <typename T>
void checkResponse(const std::string& outputName, const PredictResponse& response, const std::vector<T>& data, const shape_t& shape) {
ASSERT_TRUE(response.outputs().contains(outputName)) << outputName;
const auto& proto = response.outputs().at(outputName);
ASSERT_EQ(proto.tensor_content().size(), data.size() * sizeof(T));
ASSERT_EQ(proto.tensor_shape().dim_size(), shape.size());
for (size_t i = 0; i < shape.size(); ++i) {
ASSERT_EQ(proto.tensor_shape().dim(i).size(), shape[i]);
}
auto* ptr = reinterpret_cast<const T*>(proto.tensor_content().c_str());
const std::vector<T> actual(ptr, ptr + data.size());
for (size_t i = 0; i < actual.size(); i++) {
EXPECT_NEAR(actual[i], data[i], 0.001) << " i is: " << i;
}
}
template <typename T>
void checkResponse(const std::string& outputName, const PredictResponse& response, std::vector<T> data, std::function<T(T)> op) {
std::transform(data.begin(), data.end(), data.begin(), op);
ASSERT_TRUE(response.outputs().contains(outputName));
const auto& proto = response.outputs().at(outputName);
ASSERT_EQ(proto.tensor_content().size(), data.size() * sizeof(T));
ASSERT_EQ(proto.tensor_shape().dim_size(), 2);
ASSERT_EQ(proto.tensor_shape().dim(0).size(), 1);
ASSERT_EQ(proto.tensor_shape().dim(1).size(), data.size());
auto* ptr = reinterpret_cast<const T*>(proto.tensor_content().c_str());
const std::vector<T> actual(ptr, ptr + data.size());
for (size_t i = 0; i < actual.size(); i++) {
EXPECT_NEAR(actual[i], data[i], 0.001);
}
}
PredictRequest request;
PredictResponse response;
MetricRegistry registry;
MetricConfig metricConfig;
std::unique_ptr<ModelMetricReporter> reporter;
NodeLibrary library;
const std::string customNodeName = "add_sub_node";
const std::string libraryName = "add_sub_lib";
const std::string libraryPath = "/ovms/bazel-bin/src/lib_node_add_sub.so";
const std::string customNodeInputName = "input_numbers";
const std::string customNodeOutputName = "output_numbers";
static constexpr const char* pipelineInputName = "pipeline_input";
const std::string pipelineOutputName = "pipeline_output";
std::shared_ptr<const ovms::TensorInfo> dagDummyModelOutputTensorInfo;
std::shared_ptr<const ovms::TensorInfo> dagDummyModelInputTensorInfo;
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, AddSubCustomNode) {
// Most basic configuration, just process single add-sub custom node pipeline request
// input add-sub output
// O------->O------->O
const std::vector<float> inputValues{3.2, 5.7, -2.4};
this->prepareRequest(inputValues);
const float addValue = 2.5;
const float subValue = 4.8;
auto inputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t inputsInfo{{pipelineInputName, inputTensorInfo}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
auto tensorInfo = std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t outputsInfo{{pipelineOutputName, tensorInfo}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto custom_node = std::make_unique<CustomNode>(customNodeName, library,
parameters_t{
{"add_value", std::to_string(addValue)},
{"sub_value", std::to_string(subValue)}});
Pipeline pipeline(*input_node, *output_node, *this->reporter);
pipeline.connect(*input_node, *custom_node, {{pipelineInputName, customNodeInputName}});
pipeline.connect(*custom_node, *output_node, {{customNodeOutputName, pipelineOutputName}});
pipeline.push(std::move(input_node));
pipeline.push(std::move(custom_node));
pipeline.push(std::move(output_node));
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), 1);
this->checkResponse<float>(inputValues, [addValue, subValue](float value) -> float {
return value + addValue - subValue;
});
}
class EnsembleFlowCustomNodeAndDemultiplexerGatherPipelineExecutionTest : public EnsembleFlowCustomNodePipelineExecutionTest {
protected:
ConstructorEnabledModelManager modelManager;
ModelConfig config = DUMMY_MODEL_CONFIG;
CustomNodeLibraryManager manager;
NodeLibrary differentOpsLibrary;
NodeLibrary chooseMaxLibrary;
const std::string differentOpsLibraryName{"different_ops"};
const std::string chooseMaxLibraryName{"choose_max"};
const std::string differentOpsLibraryPath{"/ovms/bazel-bin/src/lib_node_perform_different_operations.so"};
const std::string chooseMaxLibraryPath{"/ovms/bazel-bin/src/lib_node_choose_maximum.so"};
const std::string pipelineInputName = "pipeline_input";
const std::string pipelineOutputName = "pipeline_output";
const std::string pipelineFactorsName = "pipeline_factors";
const std::string chooseMaxInputName = "input_tensors";
const std::string chooseMaxOutputName = "maximum_tensor";
const std::string differentOpsInputName = "input_numbers";
const std::string differentOpsFactorsInputName = "op_factors";
const std::string differentOpsOutputName = "different_ops_results";
const std::string differentOpsFactorsOutputName = "different_ops_factors";
const std::unordered_map<std::string, std::string> differentOpsOutputAlias{{differentOpsOutputName, differentOpsOutputName}};
const std::unordered_map<std::string, std::string> chooseMaxOutputAlias{{chooseMaxOutputName, chooseMaxOutputName}};
const std::string dummyNodeName = "dummy";
const std::string differentOpsNodeName{"different-ops-node"};
const std::string chooseMaxNodeName{"choose-max-node"};
const int32_t demultiplyCount = 4; // different ops library has (1,4,10) as output
void SetUp() override {
EnsembleFlowCustomNodePipelineExecutionTest::SetUp();
// increasing default nireq == 1 to speed up the tests
// in multilayered demultiplication we still will have more than
// 16 concurrent inferences
config.setNireq(16);
ASSERT_EQ(modelManager.reloadModelWithVersions(config), StatusCode::OK_RELOADED);
ASSERT_EQ(manager.loadLibrary(
differentOpsLibraryName,
getGenericFullPathForBazelOut(differentOpsLibraryPath)),
StatusCode::OK);
ASSERT_EQ(manager.getLibrary(
differentOpsLibraryName,
differentOpsLibrary),
StatusCode::OK);
ASSERT_EQ(manager.loadLibrary(
chooseMaxLibraryName,
getGenericFullPathForBazelOut(chooseMaxLibraryPath)),
StatusCode::OK);
ASSERT_EQ(manager.getLibrary(
chooseMaxLibraryName,
chooseMaxLibrary),
StatusCode::OK);
dagDummyModelOutputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
DUMMY_MODEL_SHAPE_META,
Layout{"NC"});
dagDummyModelInputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
DUMMY_MODEL_SHAPE_META,
Layout{"NC"});
}
};
TEST_F(EnsembleFlowCustomNodeAndDemultiplexerGatherPipelineExecutionTest, MultipleDemultiplexerDummyGathersIntertwinedLevels) {
// Most basic configuration, just process single add-sub custom node pipeline request
// input (differentOps dummy chooseMax ) XN output
// O-----(----->O---------->O------->O------>...----->O
const uint32_t demultiplicationLayersCount = 10;
// values chosen in a way that first chosen different ops result will be addition. all following ones will be multiplications
const std::vector<float> inputValues{0.2, 0.7, -0.4, -0.1, 0.0001, -0.8, 0.7, 0.8, 0.9, 0.1};
const std::vector<float> inputFactors{1, -1, 2, 2};
parameters_t parameters{
{"selection_criteria", "MAXIMUM_MAXIMUM"}};
// create expected output -> it is dependent from input values & DAG topology
auto expectedResult = inputValues;
std::transform(expectedResult.begin(), expectedResult.end(), expectedResult.begin(),
[demultiplicationLayersCount, inputFactors](float f) {
for (size_t iterations = 0; iterations < demultiplicationLayersCount; ++iterations) {
// input values are prepared in a way that the first layer will choose adding operation tensor
if (iterations == 0) {
f += inputFactors[0];
} else {
f *= inputFactors[2]; // different ops multiply will be chosen
}
f += 1; // dummy
}
return f;
});
PredictRequest predictRequest;
this->prepareRequest(predictRequest, inputValues, pipelineInputName);
this->prepareRequest(predictRequest, inputFactors, pipelineFactorsName);
// create pipeline
std::vector<std::unique_ptr<Node>> nodes(2 + 3 * demultiplicationLayersCount); // entry + exit + (choose + differentOps + dummy) * layerCount
const tensor_map_t inputsInfo{{pipelineInputName, dagDummyModelInputTensorInfo}, {pipelineFactorsName,
std::make_shared<ovms::TensorInfo>(pipelineFactorsName,
ovms::Precision::FP32,
ovms::Shape{1, 4},
Layout{"NC"})}};
nodes[0] = std::make_unique<EntryNode<PredictRequest>>(&predictRequest, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, dagDummyModelOutputTensorInfo}};
nodes[1] = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
size_t i = 2;
for (size_t demultiplicationLayer = 0; demultiplicationLayer < demultiplicationLayersCount; ++demultiplicationLayer) {
nodes[i++] = std::make_unique<CustomNode>(differentOpsNodeName + "-" + std::to_string(demultiplicationLayer), differentOpsLibrary, parameters_t{}, differentOpsOutputAlias, demultiplyCount);
nodes[i++] = std::make_unique<DLNode>(dummyNodeName + "-" + std::to_string(demultiplicationLayer), "dummy", std::nullopt, modelManager);
nodes[i++] = std::make_unique<CustomNode>(chooseMaxNodeName + "-" + std::to_string(demultiplicationLayer), chooseMaxLibrary, parameters, chooseMaxOutputAlias, std::nullopt, std::set<std::string>({differentOpsNodeName + "-" + std::to_string(demultiplicationLayer)}));
}
Pipeline pipeline(*nodes[0], *nodes[1], *this->reporter);
i = 2;
for (size_t demultiplicationLayer = 0; demultiplicationLayer < demultiplicationLayersCount; ++demultiplicationLayer) {
if (i == 2) { // first node after entry
pipeline.connect(*nodes[0], *nodes[i], {{pipelineFactorsName, differentOpsFactorsInputName}, {pipelineInputName, differentOpsInputName}});
} else { // node inside pipeline
pipeline.connect(*nodes[0], *nodes[i], {{pipelineFactorsName, differentOpsFactorsInputName}});
}
pipeline.connect(*nodes[i], *nodes[i + 1], {{differentOpsOutputName, DUMMY_MODEL_INPUT_NAME}});
pipeline.connect(*nodes[i + 1], *nodes[i + 2], {{DUMMY_MODEL_OUTPUT_NAME, chooseMaxInputName}});
if ((i + 3) != (2 + 3 * demultiplicationLayersCount)) { // connect different ops to choose max
pipeline.connect(*nodes[i + 2], *nodes[i + 3], {{chooseMaxOutputName, differentOpsInputName}});
} else { // if last connect to exit node
pipeline.connect(*nodes[i + 2], *nodes[1], {{chooseMaxOutputName, pipelineOutputName}});
}
i = i + 3;
}
for (auto& node : nodes) {
pipeline.push(std::move(node));
}
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), 1);
this->checkResponse(pipelineOutputName, response, expectedResult, {1, 10});
}
TEST_F(EnsembleFlowCustomNodeAndDemultiplexerGatherPipelineExecutionTest, MultipleDemultiplexerLevelsThenDummyThenMultipleGathers) {
// Most basic configuration, just process single add-sub custom node pipeline request
// input (differentOps dummy)xN chooseMax xN output
// O-----(----->O------->O---...----->O---->...----->O
const uint32_t demultiplicationLayersCount = 4;
// values chosen in a way that first chosen different ops result will be addition. all following ones will be multiplications
const std::vector<float> inputValues{0.2, 0.7, -0.4, -0.1, 0.0001, -0.8, 0.7, 0.8, 0.9, 0.1};
const std::vector<float> inputFactors{1, -1, 2, 2};
parameters_t parameters{
{"selection_criteria", "MAXIMUM_MAXIMUM"}};
// create expected output -> it is dependent from input values & DAG topology
auto expectedResult = inputValues;
std::transform(expectedResult.begin(), expectedResult.end(), expectedResult.begin(),
[demultiplicationLayersCount, inputFactors](float f) {
for (size_t iterations = 0; iterations < demultiplicationLayersCount; ++iterations) {
// input values are prepared in a way that the first layer will choose adding operation tensor
if (iterations == 0) {
f += inputFactors[0];
} else {
f *= inputFactors[2]; // different ops multiply will be chosen
}
f += 1; // dummy
}
return f;
});
PredictRequest predictRequest;
this->prepareRequest(predictRequest, inputValues, pipelineInputName);
this->prepareRequest(predictRequest, inputFactors, pipelineFactorsName);
// create pipeline
size_t nodesCount = 2 + 3 * demultiplicationLayersCount; // entry + exit + (choose + differentOps + dummy) * layerCount
std::vector<std::unique_ptr<Node>> nodes(nodesCount);
const tensor_map_t inputsInfo{{pipelineInputName, dagDummyModelInputTensorInfo}, {pipelineFactorsName,
std::make_shared<ovms::TensorInfo>(pipelineFactorsName,
ovms::Precision::FP32,
ovms::Shape{1, 4},
Layout{"NC"})}};
nodes[0] = std::make_unique<EntryNode<PredictRequest>>(&predictRequest, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, dagDummyModelOutputTensorInfo}};
nodes[nodesCount - 1] = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
size_t i = 1;
for (size_t demultiplicationLayer = 0; demultiplicationLayer < demultiplicationLayersCount; ++demultiplicationLayer) {
nodes[i++] = std::make_unique<CustomNode>(differentOpsNodeName + "-" + std::to_string(demultiplicationLayer), differentOpsLibrary, parameters_t{}, differentOpsOutputAlias, demultiplyCount);
nodes[i++] = std::make_unique<DLNode>(dummyNodeName + "-" + std::to_string(demultiplicationLayer), "dummy", std::nullopt, modelManager);
nodes[nodesCount - 1 - (i / 2)] = std::make_unique<CustomNode>(chooseMaxNodeName + "-" + std::to_string(demultiplicationLayer), chooseMaxLibrary, parameters, chooseMaxOutputAlias, std::nullopt, std::set<std::string>({differentOpsNodeName + "-" + std::to_string(demultiplicationLayer)}));
}
Pipeline pipeline(*nodes[0], *nodes[nodesCount - 1], *this->reporter);
i = 1;
for (size_t demultiplicationLayer = 0; demultiplicationLayer < demultiplicationLayersCount; ++demultiplicationLayer) {
if (i == 1) { // first node after entry needs to connect to entry
pipeline.connect(*nodes[0], *nodes[i], {{pipelineFactorsName, differentOpsFactorsInputName}, {pipelineInputName, differentOpsInputName}});
}
pipeline.connect(*nodes[i], *nodes[i + 1], {{differentOpsOutputName, DUMMY_MODEL_INPUT_NAME}});
// pass factors further if +2 node is differentOps
if (demultiplicationLayer != demultiplicationLayersCount - 1) {
pipeline.connect(*nodes[i], *nodes[i + 2], {{differentOpsFactorsOutputName, differentOpsFactorsInputName}});
}
// in between different ops & dummy node
if (demultiplicationLayer != demultiplicationLayersCount - 1) { // all but last dummy connect to differentOps node
pipeline.connect(*nodes[i + 1], *nodes[i + 2], {{DUMMY_MODEL_OUTPUT_NAME, differentOpsInputName}});
} else { // last dummy connects to chooseMax node
pipeline.connect(*nodes[i + 1], *nodes[i + 2], {{DUMMY_MODEL_OUTPUT_NAME, chooseMaxInputName}});
}
if (demultiplicationLayer != 0) { // in between choose max nodes
pipeline.connect(*nodes[nodesCount - 1 - (demultiplicationLayer + 1)],
*nodes[nodesCount - 1 - demultiplicationLayer], {{chooseMaxOutputName, chooseMaxInputName}});
} else { // connect last choose max to exit node
pipeline.connect(*nodes[nodesCount - 1 - (demultiplicationLayer + 1)],
*nodes[nodesCount - 1 - demultiplicationLayer], {{chooseMaxOutputName, pipelineOutputName}});
}
i = i + 2;
}
for (auto& node : nodes) {
pipeline.push(std::move(node));
}
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), 1);
this->checkResponse(pipelineOutputName, response, expectedResult, {1, 10});
}
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, SeriesOfCustomNodes) {
constexpr int N = 100;
constexpr int PARAMETERS_PAIRS_COUNT = 2;
static_assert(PARAMETERS_PAIRS_COUNT > 0);
static_assert(N > PARAMETERS_PAIRS_COUNT);
static_assert((N % PARAMETERS_PAIRS_COUNT) == 0);
// input add-sub x N output
// O------->O->O...O->O------->O
const std::vector<float> inputValues{3.2, 5.7, -2.4};
this->prepareRequest(inputValues);
const std::array<float, PARAMETERS_PAIRS_COUNT> addValues{1.5, -2.4};
const std::array<float, PARAMETERS_PAIRS_COUNT> subValues{-5.1, 1.9};
auto inputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t inputsInfo{{pipelineInputName, inputTensorInfo}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
auto tensorInfo = std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t outputsInfo{{pipelineOutputName, tensorInfo}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
std::unique_ptr<CustomNode> custom_nodes[N];
for (int i = 0; i < N; i++) {
custom_nodes[i] = std::make_unique<CustomNode>(customNodeName + std::to_string(i), library,
parameters_t{
{"add_value", std::to_string(addValues[i % PARAMETERS_PAIRS_COUNT])},
{"sub_value", std::to_string(subValues[i % PARAMETERS_PAIRS_COUNT])}});
}
Pipeline pipeline(*input_node, *output_node, *this->reporter);
pipeline.connect(*input_node, *(custom_nodes[0]), {{pipelineInputName, customNodeInputName}});
pipeline.connect(*(custom_nodes[N - 1]), *output_node, {{customNodeOutputName, pipelineOutputName}});
for (int i = 0; i < N - 1; i++) {
pipeline.connect(*(custom_nodes[i]), *(custom_nodes[i + 1]), {{customNodeOutputName, customNodeInputName}});
}
pipeline.push(std::move(input_node));
pipeline.push(std::move(output_node));
for (auto& custom_node : custom_nodes) {
pipeline.push(std::move(custom_node));
}
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), 1);
this->checkResponse<float>(inputValues, [N, addValues, subValues, PARAMETERS_PAIRS_COUNT](float value) -> float {
for (int i = 0; i < PARAMETERS_PAIRS_COUNT; i++) {
value += (N / PARAMETERS_PAIRS_COUNT) * addValues[i];
value -= (N / PARAMETERS_PAIRS_COUNT) * subValues[i];
}
return value;
});
}
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, ParallelCustomNodes) {
constexpr int N = 200;
constexpr int PARAMETERS_PAIRS_COUNT = 5;
static_assert(PARAMETERS_PAIRS_COUNT > 0);
static_assert(N > PARAMETERS_PAIRS_COUNT);
static_assert((N % PARAMETERS_PAIRS_COUNT) == 0);
/* input add-sub x N output
O---------->O------------->O
... ... /\
L---------->O-------------_|
*/
const std::vector<float> inputValues{9.1, -3.7, 22.2};
this->prepareRequest(inputValues);
const std::array<float, PARAMETERS_PAIRS_COUNT> addValues{4.5, 0.2, -0.6, 0.4, -2.5};
const std::array<float, PARAMETERS_PAIRS_COUNT> subValues{8.5, -3.2, 10.0, -0.5, 2.4};
auto inputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"});
const tensor_map_t inputsInfo{{pipelineInputName, inputTensorInfo}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
tensor_map_t outputsInfo;
for (size_t i = 0; i < N; ++i) {
const std::string outputName = pipelineOutputName + std::to_string(i);
outputsInfo.emplace(outputName,
std::make_shared<ovms::TensorInfo>(outputName,
ovms::Precision::FP32,
ovms::Shape{1, 3},
Layout{"NC"}));
}
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
Pipeline pipeline(*input_node, *output_node, *this->reporter);
std::unique_ptr<CustomNode> custom_nodes[N];
for (int i = 0; i < N; i++) {
custom_nodes[i] = std::make_unique<CustomNode>(customNodeName + std::to_string(i), library,
parameters_t{
{"add_value", std::to_string(addValues[i % PARAMETERS_PAIRS_COUNT])},
{"sub_value", std::to_string(subValues[i % PARAMETERS_PAIRS_COUNT])}});
pipeline.connect(*input_node, *(custom_nodes[i]),
{{pipelineInputName, customNodeInputName}});
pipeline.connect(*(custom_nodes[i]), *output_node,
{{customNodeOutputName, pipelineOutputName + std::to_string(i)}});
pipeline.push(std::move(custom_nodes[i]));
}
pipeline.push(std::move(input_node));
pipeline.push(std::move(output_node));
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), N);
for (int i = 0; i < N; i++) {
this->checkResponse<float>(
pipelineOutputName + std::to_string(i),
inputValues,
[i, addValues, subValues, PARAMETERS_PAIRS_COUNT](float value) -> float {
value += addValues[i % PARAMETERS_PAIRS_COUNT];
value -= subValues[i % PARAMETERS_PAIRS_COUNT];
return value;
});
}
}
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, CustomAndDLNodes) {
// input add-sub1 dummy add-sub2 output
// O------->O------O--------O------>O
ConstructorEnabledModelManager modelManager;
ModelConfig config = DUMMY_MODEL_CONFIG;
modelManager.reloadModelWithVersions(config);
const std::vector<float> inputValues{
4, 1.5, -5, -2.5, 9.3, 0.3, -0.15, 7.4, 5.2, -2.4};
this->prepareRequest(inputValues);
const float addValues[] = {-0.85, 30.2};
const float subValues[] = {1.35, -28.5};
const tensor_map_t inputsInfo{{pipelineInputName, dagDummyModelInputTensorInfo}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, dagDummyModelOutputTensorInfo}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto model_node = std::make_unique<DLNode>(
"dummy_node",
"dummy",
std::nullopt,
modelManager);
std::unique_ptr<CustomNode> custom_node[] = {
std::make_unique<CustomNode>(customNodeName + "_0", library,
parameters_t{
{"add_value", std::to_string(addValues[0])},
{"sub_value", std::to_string(subValues[0])}}),
std::make_unique<CustomNode>(customNodeName + "_1", library,
parameters_t{
{"add_value", std::to_string(addValues[1])},
{"sub_value", std::to_string(subValues[1])}})};
Pipeline pipeline(*input_node, *output_node, *this->reporter);
pipeline.connect(*input_node, *(custom_node[0]), {{pipelineInputName, customNodeInputName}});
pipeline.connect(*(custom_node[0]), *model_node, {{customNodeOutputName, DUMMY_MODEL_INPUT_NAME}});
pipeline.connect(*model_node, *(custom_node[1]), {{DUMMY_MODEL_OUTPUT_NAME, customNodeInputName}});
pipeline.connect(*(custom_node[1]), *output_node, {{customNodeOutputName, pipelineOutputName}});
pipeline.push(std::move(input_node));
pipeline.push(std::move(custom_node[0]));
pipeline.push(std::move(custom_node[1]));
pipeline.push(std::move(model_node));
pipeline.push(std::move(output_node));
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(response.outputs().size(), 1);
this->checkResponse<float>(inputValues, [addValues, subValues](float value) -> float {
return value + DUMMY_ADDITION_VALUE + addValues[0] + addValues[1] - subValues[0] - subValues[1];
});
}
struct LibraryWithScalarOutput {
static constexpr float libraryScalarNodeAddValue = 2.1f;
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor* inputs, int inputsCount, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
if (inputsCount != 1)
return 1;
*handle = (struct CustomNodeTensor*)malloc(sizeof(struct CustomNodeTensor));
*outputsNum = 1;
(*handle)->name = "output_numbers";
(*handle)->precision = CustomNodeTensorPrecision::FP32;
(*handle)->dims = (uint64_t*)malloc(0);
(*handle)->dimsCount = 0;
(*handle)->data = (uint8_t*)malloc(sizeof(float));
*((float*)(*handle)->data) = *((float*)inputs[0].data) + libraryScalarNodeAddValue;
(*handle)->dataBytes = sizeof(float);
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, CustomNodeScalarsAndScalarDLNodeInTheMiddle) {
// input CN DL CN output
// O------->O------->O--------O------>O
// +const passthru +const
ConstructorEnabledModelManager modelManager;
ModelConfig config = SCALAR_MODEL_CONFIG;
modelManager.reloadModelWithVersions(config);
const std::vector<float> inputValues{5.4f};
const std::vector<float> expectedOutputValues{inputValues[0] + LibraryWithScalarOutput::libraryScalarNodeAddValue * 2};
this->prepareRequest(this->request, inputValues, pipelineInputName, {});
((*this->request.mutable_inputs())[pipelineInputName]).mutable_tensor_shape()->Clear();
const tensor_map_t inputsInfo{{pipelineInputName, std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."})}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."})}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto model_node = std::make_unique<DLNode>(
"scalar_node",
"scalar",
std::nullopt,
modelManager);
std::unique_ptr<CustomNode> custom_node[] = {
std::make_unique<CustomNode>("scalar_node_0", createLibraryMock<LibraryWithScalarOutput>(),
parameters_t{}),
std::make_unique<CustomNode>("scalar_node_1", createLibraryMock<LibraryWithScalarOutput>(),
parameters_t{})};
Pipeline pipeline(*input_node, *output_node, *this->reporter);
pipeline.connect(*input_node, *(custom_node[0]), {{pipelineInputName, "anything"}});
pipeline.connect(*(custom_node[0]), *model_node, {{"output_numbers", SCALAR_MODEL_INPUT_NAME}});
pipeline.connect(*model_node, *(custom_node[1]), {{SCALAR_MODEL_OUTPUT_NAME, "anything"}});
pipeline.connect(*(custom_node[1]), *output_node, {{"output_numbers", pipelineOutputName}});
pipeline.push(std::move(input_node));
pipeline.push(std::move(custom_node[0]));
pipeline.push(std::move(custom_node[1]));
pipeline.push(std::move(model_node));
pipeline.push(std::move(output_node));
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(this->response.mutable_outputs()->count(pipelineOutputName), 1);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_shape()->dim_size(), 0);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->size(), sizeof(float));
ASSERT_EQ(*(float*)(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->data()), expectedOutputValues[0]);
}
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, DLNodeScalarsAndScalarCustomNodeInTheMiddle) {
// input DL CN DL output
// O------->O------->O--------O------>O
// passthru +const passthru
ConstructorEnabledModelManager modelManager;
ModelConfig config = SCALAR_MODEL_CONFIG;
modelManager.reloadModelWithVersions(config);
const std::vector<float> inputValues{5.4f};
const std::vector<float> expectedOutputValues{inputValues[0] + LibraryWithScalarOutput::libraryScalarNodeAddValue};
this->prepareRequest(this->request, inputValues, pipelineInputName, {});
((*this->request.mutable_inputs())[pipelineInputName]).mutable_tensor_shape()->Clear();
const tensor_map_t inputsInfo{{pipelineInputName, std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."})}};
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
const tensor_map_t outputsInfo{{pipelineOutputName, std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."})}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto custom_node = std::make_unique<CustomNode>("scalar_node_0", createLibraryMock<LibraryWithScalarOutput>(),
parameters_t{});
std::unique_ptr<DLNode> model_node[] = {
std::make_unique<DLNode>("scalar_model_0", "scalar", std::nullopt, modelManager),
std::make_unique<DLNode>("scalar_model_1", "scalar", std::nullopt, modelManager)};
Pipeline pipeline(*input_node, *output_node, *this->reporter);
pipeline.connect(*input_node, *(model_node[0]), {{pipelineInputName, SCALAR_MODEL_INPUT_NAME}});
pipeline.connect(*(model_node[0]), *custom_node, {{SCALAR_MODEL_OUTPUT_NAME, "anything"}});
pipeline.connect(*custom_node, *(model_node[1]), {{"output_numbers", SCALAR_MODEL_INPUT_NAME}});
pipeline.connect(*(model_node[1]), *output_node, {{SCALAR_MODEL_OUTPUT_NAME, pipelineOutputName}});
pipeline.push(std::move(input_node));
pipeline.push(std::move(model_node[0]));
pipeline.push(std::move(model_node[1]));
pipeline.push(std::move(custom_node));
pipeline.push(std::move(output_node));
ASSERT_EQ(pipeline.execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(this->response.mutable_outputs()->count(pipelineOutputName), 1);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_shape()->dim_size(), 0);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->size(), sizeof(float));
ASSERT_EQ(*(float*)(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->data()), expectedOutputValues[0]);
}
struct LibraryFailInExecute {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 1;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeExecution) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryFailInExecute>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_EXECUTION_FAILED);
}
struct LibraryCorruptedOutputHandle {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
*handle = nullptr;
*outputsNum = 5;
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeOutputsCorruptedHandle) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryCorruptedOutputHandle>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_OUTPUTS_CORRUPTED);
}
struct LibraryCorruptedOutputsNumber {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
*handle = (struct CustomNodeTensor*)malloc(5 * sizeof(struct CustomNodeTensor));
*outputsNum = 0;
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeOutputsCorruptedNumberOfOutputs) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryCorruptedOutputsNumber>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_OUTPUTS_CORRUPTED_COUNT);
}
struct LibraryMissingOutput {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
*handle = (struct CustomNodeTensor*)malloc(sizeof(struct CustomNodeTensor));
*outputsNum = 1;
(*handle)->name = "random_not_connected_output";
(*handle)->precision = CustomNodeTensorPrecision::FP32;
(*handle)->dims = (uint64_t*)malloc(sizeof(uint64_t));
(*handle)->dims[0] = 1;
(*handle)->dimsCount = 1;
(*handle)->data = (uint8_t*)malloc(sizeof(float) * sizeof(uint8_t));
(*handle)->dataBytes = sizeof(float);
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeMissingOutput) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryMissingOutput>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_MISSING_OUTPUT);
}
struct LibraryIncorrectOutputPrecision {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
*handle = (struct CustomNodeTensor*)malloc(sizeof(struct CustomNodeTensor));
*outputsNum = 1;
(*handle)->name = "output_numbers";
(*handle)->precision = CustomNodeTensorPrecision::UNSPECIFIED;
(*handle)->dims = (uint64_t*)malloc(sizeof(uint64_t));
(*handle)->dimsCount = 1;
(*handle)->data = (uint8_t*)malloc(sizeof(uint8_t));
(*handle)->dataBytes = 1;
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeOutputInvalidPrecision) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryIncorrectOutputPrecision>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_INVALID_PRECISION);
}
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, SuccessLibraryWithScalarOutput) {
const std::vector<float> inputValues{3.5f};
const std::vector<float> expectedOutputValues{inputValues[0] + LibraryWithScalarOutput::libraryScalarNodeAddValue};
auto inputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineInputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."});
const tensor_map_t inputsInfo{{pipelineInputName, inputTensorInfo}};
this->prepareRequest(this->request, inputValues, pipelineInputName, {});
((*this->request.mutable_inputs())[pipelineInputName]).mutable_tensor_shape()->Clear();
auto input_node = std::make_unique<EntryNode<PredictRequest>>(&request, inputsInfo);
auto outputTensorInfo = std::make_shared<ovms::TensorInfo>(pipelineOutputName,
ovms::Precision::FP32,
ovms::Shape{},
Layout{"..."});
const tensor_map_t outputsInfo{{pipelineOutputName, outputTensorInfo}};
auto output_node = std::make_unique<ExitNode<PredictResponse>>(&response, outputsInfo);
auto custom_node = std::make_unique<CustomNode>(
customNodeName,
createLibraryMock<LibraryWithScalarOutput>(),
parameters_t{});
auto pipeline = std::make_unique<Pipeline>(*input_node, *output_node, *this->reporter);
pipeline->connect(*input_node, *custom_node, {{pipelineInputName, customNodeInputName}});
pipeline->connect(*custom_node, *output_node, {{customNodeOutputName, pipelineOutputName}});
pipeline->push(std::move(input_node));
pipeline->push(std::move(custom_node));
pipeline->push(std::move(output_node));
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::OK);
ASSERT_EQ(this->response.mutable_outputs()->count(pipelineOutputName), 1);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_shape()->dim_size(), 0);
ASSERT_EQ(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->size(), sizeof(float));
ASSERT_EQ(*(float*)(this->response.mutable_outputs()->at(pipelineOutputName).mutable_tensor_content()->data()), expectedOutputValues[0]);
}
struct LibraryIncorrectOutputContentSize {
static int initialize(void** customNodeLibraryInternalManager, const struct CustomNodeParam* params, int paramsCount) {
return 0;
}
static int deinitialize(void* customNodeLibraryInternalManager) {
return 0;
}
static int execute(const struct CustomNodeTensor*, int, struct CustomNodeTensor** handle, int* outputsNum, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
*handle = (struct CustomNodeTensor*)malloc(sizeof(struct CustomNodeTensor));
*outputsNum = 1;
(*handle)->name = "output_numbers";
(*handle)->precision = CustomNodeTensorPrecision::FP32;
(*handle)->dims = (uint64_t*)malloc(sizeof(uint64_t));
(*handle)->dimsCount = 1;
(*handle)->data = nullptr;
(*handle)->dataBytes = 0;
return 0;
}
static int getInputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int getOutputsInfo(struct CustomNodeTensorInfo**, int*, const struct CustomNodeParam*, int, void* customNodeLibraryInternalManager) {
return 0;
}
static int release(void* ptr, void* customNodeLibraryInternalManager) {
free(ptr);
return 0;
}
};
TEST_F(EnsembleFlowCustomNodePipelineExecutionTest, FailInCustomNodeOutputInvalidContentSize) {
auto pipeline = this->prepareSingleNodePipelineWithLibraryMock<LibraryIncorrectOutputContentSize>();
ASSERT_EQ(pipeline->execute(DEFAULT_TEST_CONTEXT), StatusCode::NODE_LIBRARY_INVALID_CONTENT_SIZE);
}