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# ----------------------------------------------------------------------------
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kylin-ondevice-ai-engine-plugin-1.2.0.0/CMakeLists.txt 0000664 0000000 0000000 00000000443 15205751733 0022467 0 ustar 00root root 0000000 0000000 cmake_minimum_required(VERSION 3.16)
set(TARGET ondevice-engine-obj)
project(ondevice-engine-obj)
add_subdirectory(ondeviceembeddingengine)
add_subdirectory(ondevicenlpengine)
add_subdirectory(ondevicevisionengine)
add_subdirectory(ondeviceembeddingplugin)
add_subdirectory(ondeviceplugin)
kylin-ondevice-ai-engine-plugin-1.2.0.0/LICENSE 0000664 0000000 0000000 00000104515 15205751733 0020741 0 ustar 00root root 0000000 0000000 GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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.
kylin-ondevice-ai-engine-plugin-1.2.0.0/README.md 0000664 0000000 0000000 00000001512 15205751733 0021204 0 ustar 00root root 0000000 0000000 # kylin-ondevice-ai-engine-plugin
On-device AI engine plugins for the Kylin AI subsystem.
## Components
- **ondeviceembeddingengine** - Embedding engine for local vector inference
- **ondevicenlpengine** - NLP engine for local text generation
- **ondevicevisionengine** - Vision engine for OCR, image processing, and portrait matting
- **ondeviceembeddingplugin** - Plugin wrapper for the embedding engine
- **ondeviceplugin** - Plugin wrapper aggregating NLP and vision engines
## Build Dependencies
- CMake >= 3.16
- C++17 compiler
- libgrpc++-dev, libprotobuf-dev
- libopencv-dev
- nlohmann-json3-dev, libjsoncpp-dev
- libfmt-dev, libspdlog-dev
- libglib2.0-dev
- uuid-dev
- libkylin-ai-system-private-dev
## Build
```bash
mkdir build && cd build
cmake ..
make
```
## License
GPL-3.0-or-later. See [LICENSE](LICENSE) for details.
kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/ 0000775 0000000 0000000 00000000000 15205751733 0024547 5 ustar 00root root 0000000 0000000 kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/CMakeLists.txt 0000664 0000000 0000000 00000003332 15205751733 0027310 0 ustar 00root root 0000000 0000000 cmake_minimum_required(VERSION 3.7)
project(kylin-ondevice-embedding-engine)
set(TARGET kylin-ondevice-embedding-engine)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
include(GNUInstallDirs)
find_package(PkgConfig REQUIRED)
pkg_check_modules(UUID REQUIRED IMPORTED_TARGET uuid)
find_package(nlohmann_json REQUIRED)
find_package(OpenCV REQUIRED)
if (DEFINED TRITON_CLIENT)
find_package(TritonClient REQUIRED)
endif ()
find_package(KylinAiEngine CONFIG REQUIRED)
set(SOURCE_LIST
src/embeddingproxy.cpp
src/embeddingserver.cpp
src/task.cpp
src/utils.cpp
)
# 单独指定头文件目录
set(HEADER_FILES
src/embeddingproxy.h
src/embeddingserver.h
src/task.h
src/utils.h
)
include_directories(
${PROJECT_SOURCE_DIR}/src
${TRITON_CLIENT_PATH}
${OpenCV_INCLUDE_DIRS}
)
add_library(${TARGET} SHARED
ondeviceembeddingengine.h
ondeviceembeddingengine.cpp
${SOURCE_LIST}
${HEADER_FILES}
)
set_target_properties(${TARGET} PROPERTIES VERSION 1.0.0 SOVERSION 1)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_link_libraries(${TARGET}
PUBLIC
PkgConfig::UUID
${OpenCV_LIBS}
nlohmann_json::nlohmann_json
)
if(DEFINED TRITON_CLIENT)
target_link_libraries(${ TARGET }
PUBLIC
TritonClient::grpcclient
)
endif()
install(TARGETS ${TARGET}
DESTINATION ${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR}
)
set(HEADER_PATH kylin-ai/plugins/ai-engines)
# install(FILES ondeviceembeddingengine.h DESTINATION include/${HEADER_PATH})
if (DEFINED ENABLE_TEST)
unset(ENABLE_TEST CACHE)
endif(DEFINED ENABLE_TEST)
option(ENABLE_TEST "Build Test" OFF)
if (ENABLE_TEST)
add_subdirectory(tests)
endif (ENABLE_TEST) kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/ondeviceembeddingengine.cpp 0000664 0000000 0000000 00000005457 15205751733 0032107 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#include "ondeviceembeddingengine.h"
#include "src/embeddingserver.h"
namespace ai_engine {
namespace core_ai {
namespace embedding {
OndeviceEmbeddingEngine::OndeviceEmbeddingEngine()
: embeddingServer_(new EmbeddingServer) {}
OndeviceEmbeddingEngine::~OndeviceEmbeddingEngine() {
if (!embeddingServer_) {
delete embeddingServer_;
embeddingServer_ = nullptr;
}
}
void OndeviceEmbeddingEngine::setEmbeddingResultCallback(
EmbeddingResultCallback callback) {
if (!callback) {
std::cerr << "EmbeddingResultCallback is nullptr" << std::endl;
return;
}
embeddingServer_->setEmbeddingResultCallback(callback);
}
std::string OndeviceEmbeddingEngine::modelInfo() const {
return embeddingServer_->modelInfo();
}
// 文本向量化
result::AiEngineBaseResult OndeviceEmbeddingEngine::initTextEmbeddingModule() {
return embeddingServer_->initTextEmbeddingModule();
}
result::AiEngineBaseResult
OndeviceEmbeddingEngine::destroyTextEmbeddingModule() {
return embeddingServer_->destroyTextEmbeddingModule();
}
result::AiEngineBaseResult OndeviceEmbeddingEngine::embeddingText(
const std::string &text) {
return embeddingServer_->embeddingText(text);
}
// 图像向量化
result::AiEngineBaseResult OndeviceEmbeddingEngine::initImageEmbeddingModule() {
return embeddingServer_->initImageEmbeddingModule();
}
result::AiEngineBaseResult
OndeviceEmbeddingEngine::destroyImageEmbeddingModule() {
return embeddingServer_->destroyImageEmbeddingModule();
}
result::AiEngineBaseResult OndeviceEmbeddingEngine::embeddingImageFromImageFile(
const std::string &filePath) {
return embeddingServer_->embeddingImageFromImageFile(filePath);
}
result::AiEngineBaseResult
OndeviceEmbeddingEngine::embeddingImageFromBase64ImageData(
const std::string &fileData) {
return embeddingServer_->embeddingImageFromBase64ImageData(fileData);
}
result::AiEngineBaseResult OndeviceEmbeddingEngine::embeddingTextWithImageModel(
const std::string &text) {
return embeddingServer_->embeddingTextWithImageModel(text);
}
} // namespace embedding
} // namespace core_ai
} // namespace ai_engine kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/ondeviceembeddingengine.h 0000664 0000000 0000000 00000004206 15205751733 0031543 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#pragma once
#include
namespace ai_engine {
namespace core_ai {
namespace embedding {
class EmbeddingServer;
class OndeviceEmbeddingEngine : public AbstractEmbeddingEngine {
public:
OndeviceEmbeddingEngine();
~OndeviceEmbeddingEngine();
void setEmbeddingResultCallback(EmbeddingResultCallback callback) override;
std::string engineName() const override {
return "ondevice-embedding-engine";
}
bool isCloud() const override { return false; }
bool isBuiltInEngine() const override { return true; }
std::string modelInfo() const override;
// 文本向量化
result::AiEngineBaseResult initTextEmbeddingModule() override;
result::AiEngineBaseResult destroyTextEmbeddingModule() override;
result::AiEngineBaseResult embeddingText(const std::string &text) override;
// 图像向量化
result::AiEngineBaseResult initImageEmbeddingModule() override;
result::AiEngineBaseResult destroyImageEmbeddingModule() override;
result::AiEngineBaseResult embeddingImageFromImageFile(
const std::string &filePath) override;
result::AiEngineBaseResult embeddingImageFromBase64ImageData(
const std::string &fileData) override;
result::AiEngineBaseResult embeddingTextWithImageModel(
const std::string &text) override;
private:
EmbeddingServer *embeddingServer_ = nullptr;
};
} // namespace embedding
} // namespace core_ai
} // namespace ai_engine
kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/src/ 0000775 0000000 0000000 00000000000 15205751733 0025336 5 ustar 00root root 0000000 0000000 kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/src/embeddingproxy.cpp 0000664 0000000 0000000 00000051550 15205751733 0031070 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#include "embeddingproxy.h"
#include
#include
#include
#include
#include
#include
#include
#include
#include "utils.h"
#ifdef TRITON_CLIENT
namespace tc = triton::client;
#endif
using json = nlohmann::json;
namespace ai_engine::core_ai::embedding {
EmbeddingProxy::EmbeddingProxy() {}
bool EmbeddingProxy::createTritonClient() {
#ifdef TRITON_CLIENT
if (initTritonClient_)
return true;
tc::Error error = tc::InferenceServerGrpcClient::Create(
&grpcClient_, grpcServerUrl_, true /* verbose */);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Create http client error,"
<< error.Message() << std::endl;
return false;
}
initTritonClient_ = true;
#endif
return true;
}
EmbeddingProxyResult EmbeddingProxy::loadTextModel() {
#ifdef TRITON_CLIENT
if (initTextModel_)
return EmbeddingProxyResult(true, std::vector{});
tc::Error error = grpcClient_->LoadModel(textEmbeddingModel_);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Load text-embedding-text-model: "
<< textEmbeddingModel_ << " error"
<< " error message: " << error.Message() << std::endl;
return EmbeddingProxyResult(false, LOAD_MODEL_ERROR, error.Message());
}
initTextModel_ = true;
#endif
return EmbeddingProxyResult(true, std::vector{});
}
EmbeddingProxyResult EmbeddingProxy::loadImageModel() {
#ifdef TRITON_CLIENT
if (initImageModel_)
return EmbeddingProxyResult(true, std::vector{});
tc::Error error = grpcClient_->LoadModel(textEmbeddingImageModel_);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Load text-embedding-image-model:"
<< textEmbeddingImageModel_ << " error"
<< " error message: " << error.Message() << std::endl;
return EmbeddingProxyResult(false, LOAD_MODEL_ERROR, error.Message());
}
error = grpcClient_->LoadModel(imageEmbeddingModel_);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Load image-embedding-image-model: "
<< imageEmbeddingModel_ << " error"
<< " error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, LOAD_MODEL_ERROR, error.Message());
}
initImageModel_ = true;
#endif
return EmbeddingProxyResult(true, std::vector{});
}
std::string EmbeddingProxy::modelInfo() const {
const std::string modelInfo = R"(
{"models":{
"text_model":{
"name":"ensemble-embd_gte-base_uint8-text",
"dim":768,
"ondevice":true},
"image_model":{
"image_text_name":"ensemble-embd_cn-clip_512-uint8-text",
"image_name":"embd_cn-clip_512-uint8-image",
"dim":1024,
"ondevice":true}
}
})";
return modelInfo;
}
EmbeddingProxyResult EmbeddingProxy::embeddingText(const std::string &text) {
#ifdef TRITON_CLIENT
std::vector inputData(1);
inputData[0] = text;
std::vector shape{1};
// Initialize the inputs with the data.
tc::InferInput *input0;
tc::Error error =
tc::InferInput::Create(&input0, "product_reviews", shape, "BYTES");
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error: unable to get product_reviews,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr inputPtr;
inputPtr.reset(input0);
inputPtr->SetBinaryData(true);
error = inputPtr->AppendFromString(inputData);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to set data for "
"product_reviews,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
// Generate the outputs to be requested.
tc::InferRequestedOutput *output0;
error = tc::InferRequestedOutput::Create(&output0, "logits");
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to get logits,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr outputPtr;
outputPtr.reset(output0);
outputPtr->SetBinaryData(true);
// The inference settings. Will be using default for now.
tc::InferOptions options(textEmbeddingModel_);
options.model_version_ = textEmbeddingVersion_;
std::vector inputs = {inputPtr.get()};
std::vector outputs = {outputPtr.get()};
tc::InferResult *results;
error = grpcClient_->Infer(&results, options, inputs, outputs);
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton infer error: unable to run model,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr resultsPtr;
resultsPtr.reset(results);
// Validate the results...
if (!validateShapeAndDatatype("logits", resultsPtr, "FP32", 0)) {
std::cerr << "[Embedding proxy]: Trion infer error: embeddingText "
"validateShapeAndDatatype failed."
<< std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR,
"Trion infer error: embeddingText "
"validateShapeAndDatatype failed.");
}
std::vector shapeOut;
resultsPtr->Shape("logits", &shapeOut);
float *outputDataPtr;
const size_t outputByteSize =
textModelDim_ * 4 * shapeOut[1] *
shapeOut[0]; // 输出 1*756*768 会输出756(会变化)个768，取第一个768
// 所以是 768 * 4 * shapeOut[1] 字节
size_t recvOutputByteSize;
error = resultsPtr->RawData("logits", (const uint8_t **)&outputDataPtr,
&recvOutputByteSize);
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error: unable to get data for logits,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
if (recvOutputByteSize != outputByteSize) {
std::cerr << "[Embedding proxy]:Error: received incorrect byte size "
"for 'logits': "
<< recvOutputByteSize << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR,
"Trion infer error: embeddingText received "
"incorrect byte size.");
}
std::vector tokens;
for (size_t i = 0; i < textModelDim_; ++i) {
tokens.push_back(outputDataPtr[i]);
}
normalize(tokens);
return EmbeddingProxyResult(true, tokens);
#else
return EmbeddingProxyResult(true, {});
#endif
}
EmbeddingProxyResult EmbeddingProxy::embeddingImage(const std::string &fileData,
int fileDataType) {
#ifdef TRITON_CLIENT
cv::Mat image;
try {
if (fileDataType == 0)
image = cv::imread(fileData, cv::IMREAD_COLOR);
else if (fileDataType == 1) {
std::vector decodedImageData =
base64Decode(fileData);
image = cv::imdecode(decodedImageData, cv::IMREAD_COLOR);
}
} catch (const cv::Exception &e) {
std::cerr << "[Embedding proxy]:OpenCV Error:" << e.what() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR,
"Trion infer error: embeddingImage "
"preprocessed image failed.");
}
if (image.empty()) {
if (fileDataType == 0) {
// 输入文件路径的才打印，否则base64数据太多了
std::cerr << "[Embedding proxy]:Error:Unable to load image from:"
<< fileData << std::endl;
}
return EmbeddingProxyResult(false, TRITON_INFER_ERROR,
"Trion infer error: embeddingImage "
"preprocessed image is empty.");
}
std::vector input0_data = preprocessVision(image);
std::vector shape = {3, 224, 224};
// Initialize the inputs with the data.
tc::InferInput *input0;
tc::Error error = tc::InferInput::Create(&input0, "image", shape, "FP32");
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error:unable to get image,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr inputPtr;
inputPtr.reset(input0);
error = inputPtr->AppendRaw(reinterpret_cast(&input0_data[0]),
input0_data.size() * sizeof(float));
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error:unable to set data for image,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
// The inference settings. Will be using default for now.
tc::InferOptions options(imageEmbeddingModel_);
options.model_version_ = imageEmbeddingVersion_;
std::vector inputs = {inputPtr.get()};
// Empty output vector will request data for all the output tensors from
// the server.
std::vector outputs = {};
tc::InferResult *results;
error = grpcClient_->Infer(&results, options, inputs, outputs);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error:unable to run model,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr resultsPtr;
resultsPtr.reset(results);
// Validate the results...
if (!validateShapeAndDatatype("unnorm_image_features", resultsPtr, "FP32",
1)) {
return EmbeddingProxyResult(false, TRITON_INFER_ERROR,
"Trion infer error: embeddingImage "
"validateShapeAndDatatype failed.");
}
// Get pointers to the result returned...
float *outputData;
size_t outputByteSize;
error = resultsPtr->RawData("unnorm_image_features",
(const uint8_t **)&outputData, &outputByteSize);
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error:unable to get result data for "
"unnorm_image_features,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
if (outputByteSize != imageModelDim_ * sizeof(float)) {
std::cerr << "[Embedding proxy]:Triton error:received incorrect byte "
"size for "
"'unnorm_image_features':"
<< outputByteSize << std::endl;
return EmbeddingProxyResult(
false, TRITON_INFER_ERROR,
"Trion infer error: embeddingImage received "
"incorrect byte size.");
}
std::vector output0_data_vector(outputData,
outputData + imageModelDim_);
normalize(output0_data_vector);
return EmbeddingProxyResult{true, output0_data_vector};
#else
return EmbeddingProxyResult{true, {}};
#endif
}
EmbeddingProxyResult
EmbeddingProxy::embeddingTextWithImageModel(const std::string &text) {
#ifdef TRITON_CLIENT
std::vector input0_data(1);
input0_data[0] = text;
std::vector shape{1};
// Initialize the inputs with the data.
tc::InferInput *input0;
tc::Error error =
tc::InferInput::Create(&input0, "product_reviews", shape, "BYTES");
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error: unable to get product_reviews,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr inputPtr;
inputPtr.reset(input0);
inputPtr->SetBinaryData(true);
error = inputPtr->AppendFromString(input0_data);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to set data for "
"product_reviews,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
// Generate the outputs to be requested.
tc::InferRequestedOutput *output0;
error = tc::InferRequestedOutput::Create(&output0, "unnorm_text_features");
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to get "
"unnorm_text_features,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr outputPtr;
outputPtr.reset(output0);
outputPtr->SetBinaryData(true);
// The inference settings. Will be using default for now.
tc::InferOptions options(textEmbeddingImageModel_);
options.model_version_ = textEmbeddingImageVersion_;
// options.priority_ = 3;
std::vector inputs = {inputPtr.get()};
std::vector outputs = {outputPtr.get()};
tc::InferResult *results;
error = grpcClient_->Infer(&results, options, inputs, outputs);
if (!error.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to run model,"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
std::shared_ptr resultsPtr;
resultsPtr.reset(results);
// Validate the results...
if (!validateShapeAndDatatype("unnorm_text_features", resultsPtr, "FP32",
1)) {
return EmbeddingProxyResult(
false, TRITON_INFER_ERROR,
"Trion infer error: embeddingTextWithImageModel "
"validateShapeAndDatatype failed.");
}
float *outputDataPtr;
size_t outputByteSize =
imageModelDim_ * 4; // 输出 1024 个 float 所以是 1024 * 4 字节
size_t recvOutputByteSize;
error = resultsPtr->RawData("unnorm_text_features",
(const uint8_t **)&outputDataPtr,
&recvOutputByteSize);
if (!error.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error: unable to get result data for "
"unnorm_text_features"
<< "error message:" << error.Message() << std::endl;
return EmbeddingProxyResult(false, TRITON_INFER_ERROR, error.Message());
}
if (recvOutputByteSize != outputByteSize) {
std::cerr
<< "[Embedding proxy]:Error: received incorrect byte size for "
"'unnorm_text_features': "
<< recvOutputByteSize << std::endl;
return EmbeddingProxyResult(
false, TRITON_INFER_ERROR,
"Trion infer error: embeddingTextWithImageModel received "
"incorrect byte size");
}
std::vector tokens;
// std::cout << "unnorm_text_features: ";
for (size_t i = 0; i < imageModelDim_; ++i) {
// std::cout << outputDataPtr[i] << " ";
tokens.push_back(outputDataPtr[i]);
}
// std::cout << std::endl;
normalize(tokens);
return EmbeddingProxyResult{true, tokens};
#else
return EmbeddingProxyResult{true, {}};
#endif
}
#ifdef TRITON_CLIENT
bool EmbeddingProxy::validateShapeAndDatatype(
const std::string &name, std::shared_ptr result,
const std::string &expectedDataType, const int modelSwitch) {
std::vector shape;
tc::Error err;
err = result->Shape(name, &shape);
if (!err.IsOk()) {
std::cerr << "[Embedding proxy]:Triton error: unable to get shape for"
<< name << std::endl;
return false;
}
// Validate shape
if (modelSwitch == 0) {
if ((shape.size() != 3) || (shape[2] != textModelDim_)) {
std::cerr
<< "[Embedding proxy]:Error: received incorrect shapes for "
<< name << std::endl;
return false;
}
} else if (modelSwitch == 1) {
if (shape != std::vector{imageModelDim_}) {
std::cerr
<< "[Embedding proxy]:Error: received incorrect shapes for "
<< name << std::endl;
return false;
}
}
std::string dataType;
err = result->Datatype(name, &dataType);
if (!err.IsOk()) {
std::cerr
<< "[Embedding proxy]:Triton error: unable to get dataType for"
<< name << std::endl;
return false;
}
// Validate dataType
if (dataType.compare(expectedDataType) != 0) {
std::cerr << "[Embedding proxy]:Error: received incorrect dataType for "
<< name << ": " << dataType << std::endl;
return false;
}
return true;
}
#endif
std::vector
EmbeddingProxy::base64Decode(const std::string &input) {
std::vector output;
const std::string base64Chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz"
"0123456789+/";
int val = 0, valb = -8;
for (auto c : input) {
if (std::isspace(c)) {
continue;
}
if (c == '=') {
break;
}
const auto ch = std::find(base64Chars.begin(), base64Chars.end(), c);
if (ch == base64Chars.end()) {
std::cerr << "[Embedding proxy]:parse base64Decode error!"
<< std::endl;
throw std::runtime_error("Invalid base64 character");
}
val = (val << 6) + (ch - base64Chars.begin());
valb += 6;
if (valb >= 0) {
output.push_back(static_cast((val >> valb) & 0xFF));
valb -= 8;
}
}
return output;
}
std::vector EmbeddingProxy::imageToFloatVector(const cv::Mat &image) {
std::vector floatVector;
// 确保图像不为空且为三通道图像
if (image.empty() || image.channels() != 3) {
std::cerr << "[Embedding proxy]:imageToFloatVector error:Image is "
"empty or not 3 channels, "
"can't convert to vector"
<< std::endl;
return floatVector;
}
// 将每个通道的像素值添加到向量中
for (int c = 0; c < image.channels(); ++c) {
for (int i = 0; i < image.cols; ++i) {
for (int j = 0; j < image.rows; ++j) {
float pixelValue = image.at(i, j)[c];
floatVector.push_back(pixelValue);
}
}
}
return floatVector;
}
std::vector EmbeddingProxy::preprocessVision(cv::Mat &image) {
const int width = 224;
const int height = 224;
const cv::Scalar mean = cv::Scalar(0.48145466, 0.4578275, 0.40821073);
const cv::Scalar deviation = cv::Scalar(0.26862954, 0.26130258, 0.27577711);
cv::resize(image, image, cv::Size(width, height), cv::INTER_CUBIC);
cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
// Normalize
image.convertTo(image, CV_32FC3, 1.0 / 255.0);
cv::subtract(image, mean, image);
cv::divide(image, deviation, image);
return imageToFloatVector(image);
}
} // namespace ai_engine::core_ai::embedding kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/src/embeddingproxy.h 0000664 0000000 0000000 00000007014 15205751733 0030531 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#pragma once
#ifdef TRITON_CLIENT
#include
#endif
#include
#include
#include
namespace ai_engine::core_ai::embedding {
enum ERROR_CODE : int {
SYSTEM_ERR = -1,
SUCCESS = 0,
LOAD_MODEL_ERROR,
CREATE_TRITON_CLIENT_ERROR,
TRITON_INFER_ERROR,
ADD_EMBEDDING_TASK_ERROR,
};
typedef struct EmbeddingProxyError {
int code;
std::string message;
} EmbeddingProxyError;
typedef struct EmbeddingProxyResult {
bool success;
EmbeddingProxyError error;
std::vector res;
EmbeddingProxyResult() {}
EmbeddingProxyResult(bool success, std::vector res)
: success(success), res(res), error{SUCCESS, "Success"} {}
EmbeddingProxyResult(bool success, int code, std::string message)
: success(success), res({}), error({code, message}) {}
} EmbeddingProxyResult;
class EmbeddingProxy {
public:
EmbeddingProxy();
bool createTritonClient();
EmbeddingProxyResult loadTextModel();
EmbeddingProxyResult loadImageModel();
std::string modelInfo() const;
EmbeddingProxyResult embeddingText(const std::string &text);
EmbeddingProxyResult embeddingImage(
const std::string &fileData,
int fileDataType); // dataType
// 区分图片数据类型，0:输入为图片路径，1：输入base64图片格式
EmbeddingProxyResult embeddingTextWithImageModel(const std::string &text);
private:
// int modelSwitch
// 由于文本和图像模型对于输出shape的检查方式不同，所以用这个参数区分一下
// modelSwitch 0:text model , 1: image model
#ifdef TRITON_CLIENT
bool validateShapeAndDatatype(
const std::string &name,
std::shared_ptr result,
const std::string &expectedDataType, const int modelSwitch);
#endif
std::vector base64Decode(const std::string &input);
std::vector imageToFloatVector(const cv::Mat &image);
std::vector preprocessVision(cv::Mat &image);
private:
bool initTritonClient_ = false;
bool initTextModel_ = false;
bool initImageModel_ = false;
private:
const std::string textEmbeddingModel_ = "ensemble-embd_gte-base_uint8-text";
const std::string textEmbeddingVersion_ = "";
const int textModelDim_ = 768;
const std::string textEmbeddingImageModel_ =
"ensemble-embd_cn-clip_512-uint8-text";
const std::string textEmbeddingImageVersion_ = "";
const std::string imageEmbeddingModel_ = "embd_cn-clip_512-uint8-image";
const std::string imageEmbeddingVersion_ = "";
const int imageModelDim_ = 1024;
const std::string grpcServerUrl_ = "localhost:8001";
#ifdef TRITON_CLIENT
std::unique_ptr grpcClient_;
#endif
};
} // namespace ai_engine::core_ai::embedding kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/src/embeddingserver.cpp 0000664 0000000 0000000 00000023075 15205751733 0031216 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#include "embeddingserver.h"
#include
#include "embeddingproxy.h"
#include "task.h"
namespace ai_engine::core_ai::embedding {
EmbeddingServer::EmbeddingServer()
: embeddingProxy_(std::make_unique()) {
startServer_ = true;
processTaskThread_ =
std::thread(&EmbeddingServer::processTaskInThread, this);
}
EmbeddingServer::~EmbeddingServer() {
startServer_ = false;
taskQueueCv_.notify_all();
if (processTaskThread_.joinable()) processTaskThread_.join();
}
void EmbeddingServer::setEmbeddingResultCallback(
EmbeddingResultCallback callback) {
callback_ = std::move(callback);
}
void EmbeddingServer::processTaskInThread() {
while (startServer_) {
EmbeddingTask task;
std::unique_lock lock(mutex_);
taskQueueCv_.wait(
lock, [this]() { return !tasks_.empty() || !startServer_; });
if (!startServer_) {
std::cout << "[Embedding server]: Embedding servcer stop"
<< std::endl;
break;
}
std::unique_lock lockTask(taskMutex_);
if (!tasks_.empty()) {
task = tasks_.top();
tasks_.pop();
}
// 阻塞到有任务之后，开始进行向量化任务
EmbeddingProxyResult result = doEmbeddingInThread(task);
if (callbacks_.find(task.id()) != callbacks_.end()) {
std::cout << "[Embedding server]:Task callback id: " << task.id()
<< "found " << std::endl;
callbacks_[task.id()](EmbeddingResult{
result.success,
ai_engine::EngineError(result.error.code, result.error.message),
"ondevice embedding engine", result.res});
callbacks_.erase(task.id());
}
}
}
EmbeddingProxyResult EmbeddingServer::doEmbeddingInThread(
const EmbeddingTask &task) {
EmbeddingProxyResult result;
switch (task.type()) {
case EmbeddingTask::EmbeddingDataType::Text:
std::cout << "[Embedding server]:Embedding text start" << std::endl;
result = embeddingProxy_->embeddingText(task.getData());
break;
case EmbeddingTask::EmbeddingDataType::TextByImageModel:
std::cout
<< "[Embedding server]:Embedding text with imageModel start"
<< std::endl;
result =
embeddingProxy_->embeddingTextWithImageModel(task.getData());
break;
case EmbeddingTask::EmbeddingDataType::Image:
std::cout
<< "[Embedding server]:Embedding image from imageFile start"
<< std::endl;
result = embeddingProxy_->embeddingImage(task.getData(), 0);
break;
case EmbeddingTask::EmbeddingDataType::ImageBase64:
std::cout << "[Embedding server]:Embedding image from "
"base6ImageData start"
<< std::endl;
result = embeddingProxy_->embeddingImage(task.getData(), 1);
break;
default:
std::cerr << "[Embedding server]:Task type error!" << std::endl;
}
return result;
}
bool EmbeddingServer::addTask(const EmbeddingTask &task) {
if (task.type() == EmbeddingTask::EmbeddingDataType::Text) {
// 文本向量化模型任务
if (!initTextEmbeddingModule().success) {
std::cerr << "[Embedding server]:Text embedding module init error!"
<< std::endl;
return false;
}
} else {
// 图像向量化模型任务
if (!initImageEmbeddingModule().success) {
std::cerr << "[Embedding server]:Image embedding module init error!"
<< std::endl;
return false;
}
}
std::unique_lock lock(taskMutex_);
callbacks_.insert(make_pair(task.id(), callback_));
tasks_.push(task);
taskQueueCv_.notify_all();
return true;
}
std::string EmbeddingServer::modelInfo() const {
return embeddingProxy_->modelInfo();
}
bool EmbeddingServer::createTritonClient() {
return embeddingProxy_->createTritonClient();
}
result::AiEngineBaseResult EmbeddingServer::initTextEmbeddingModule() {
if (!createTritonClient()) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(CREATE_TRITON_CLIENT_ERROR,
"Create triton client error!")};
}
EmbeddingProxyResult result = embeddingProxy_->loadTextModel();
return result::AiEngineBaseResult{
result.success,
ai_engine::EngineError(result.error.code, result.error.message)};
}
result::AiEngineBaseResult EmbeddingServer::destroyTextEmbeddingModule() {
// 目前不需要我们自己卸载模型，所以这里是写死的
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
result::AiEngineBaseResult EmbeddingServer::embeddingText(
const std::string &text) {
if (!callback_) {
std::cerr << "[Embedding server]:Callback is nullptr!" << std::endl;
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Callback is nullptr!")};
}
bool res =
addTask(EmbeddingTask(text, EmbeddingTask::EmbeddingDataType::Text));
if (!res) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Add embedding task error!")};
}
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
result::AiEngineBaseResult EmbeddingServer::initImageEmbeddingModule() {
if (!createTritonClient()) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(CREATE_TRITON_CLIENT_ERROR,
"Create triton client error!")};
}
EmbeddingProxyResult result = embeddingProxy_->loadImageModel();
return result::AiEngineBaseResult{
result.success,
ai_engine::EngineError(result.error.code, result.error.message)};
}
result::AiEngineBaseResult EmbeddingServer::destroyImageEmbeddingModule() {
// 目前不需要我们自己卸载模型，所以这里是写死的
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
result::AiEngineBaseResult EmbeddingServer::embeddingImageFromImageFile(
const std::string &filePath) {
if (!callback_) {
std::cerr << "[Embedding server]:Callback is nullptr!" << std::endl;
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Callback is nullptr!")};
}
bool res = addTask(
EmbeddingTask(filePath, EmbeddingTask::EmbeddingDataType::Image));
if (!res) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Add embedding task error!")};
}
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
result::AiEngineBaseResult EmbeddingServer::embeddingImageFromBase64ImageData(
const std::string &fileData) {
if (!callback_) {
std::cerr << "[Embedding server]:Callback is nullptr!" << std::endl;
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Callback is nullptr!")};
}
bool res = addTask(
EmbeddingTask(fileData, EmbeddingTask::EmbeddingDataType::ImageBase64));
if (!res) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Add embedding task error!")};
}
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
result::AiEngineBaseResult EmbeddingServer::embeddingTextWithImageModel(
const std::string &text) {
if (!callback_) {
std::cerr << "[Embedding server]:Callback is nullptr!" << std::endl;
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Callback is nullptr!")};
}
bool res = addTask(EmbeddingTask(
text, EmbeddingTask::EmbeddingDataType::TextByImageModel));
if (!res) {
return result::AiEngineBaseResult{
false, ai_engine::EngineError(ADD_EMBEDDING_TASK_ERROR,
"Add embedding task error!")};
}
return result::AiEngineBaseResult{true,
ai_engine::EngineError(0, "Success")};
}
} // namespace ai_engine::core_ai::embedding kylin-ondevice-ai-engine-plugin-1.2.0.0/ondeviceembeddingengine/src/embeddingserver.h 0000664 0000000 0000000 00000004500 15205751733 0030653 0 ustar 00root root 0000000 0000000 /*
* Copyright (C) 2024 KylinSoft Co., Ltd.
*
* This program is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* this program. If not, see .
*/
#pragma once
#include
#include
#include